From 9c32e47c30fe606b9bb5120127eec37d08e90c9a Mon Sep 17 00:00:00 2001
From: rezayaghobi-dev <rezay5639@gmail.com>
Date: Sat, 11 Jul 2026 17:17:52 +0330
Subject: [PATCH] Initial commit: housing regression, MNIST classification,
 linear models

---
 .gitignore                                    |    18 +
 README.md                                     |    27 +
 .../classification-project/README.md          |    36 +
 .../classification.ipynb                      |  7407 +++++++
 .../classification_notebook_polished.ipynb    |  3076 +++
 .../housing-price-regression/README.md        |    34 +
 .../housing-ml-notebook.ipynb                 | 15920 ++++++++++++++++
 .../housing_ml_notebook_polished.ipynb        |  1475 ++
 projects/notebooks/linear-models/README.md    |    26 +
 .../linear_models_notebook.ipynb              |  7471 ++++++++
 .../linear_models_notebook_polished.ipynb     |  1542 ++
 11 files changed, 37032 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 README.md
 create mode 100644 projects/notebooks/classification-project/README.md
 create mode 100644 projects/notebooks/classification-project/classification.ipynb
 create mode 100644 projects/notebooks/classification-project/classification_notebook_polished.ipynb
 create mode 100644 projects/notebooks/housing-price-regression/README.md
 create mode 100644 projects/notebooks/housing-price-regression/housing-ml-notebook.ipynb
 create mode 100644 projects/notebooks/housing-price-regression/housing_ml_notebook_polished.ipynb
 create mode 100644 projects/notebooks/linear-models/README.md
 create mode 100644 projects/notebooks/linear-models/linear_models_notebook.ipynb
 create mode 100644 projects/notebooks/linear-models/linear_models_notebook_polished.ipynb

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..9cd5461
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,18 @@
+# Python
+__pycache__/
+*.py[cod]
+.venv/
+venv/
+
+# Jupyter
+.ipynb_checkpoints/
+
+# Model artifacts and data — regeneratable, don't belong in git
+*.pkl
+*.joblib
+datasets/
+*.csv
+
+# OS / editor
+.DS_Store
+.vscode/
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..a13714b
--- /dev/null
+++ b/README.md
@@ -0,0 +1,27 @@
+# Machine Learning Portfolio
+
+A collection of end-to-end machine learning projects covering regression, classification, and linear models built from first principles.
+
+![Python](https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white)
+![scikit-learn](https://img.shields.io/badge/scikit--learn-F7931E?style=flat&logo=scikit-learn&logoColor=white)
+![pandas](https://img.shields.io/badge/pandas-150458?style=flat&logo=pandas&logoColor=white)
+![NumPy](https://img.shields.io/badge/NumPy-013243?style=flat&logo=numpy&logoColor=white)
+
+## Projects
+
+| Project | Task | Highlight |
+|---|---|---|
+| [housing-price-regression](housing-price-regression/) | Regression | Full pipeline (EDA → feature engineering → tuning → evaluation), test R² 0.72 |
+| [classification-project](classification-project/) | Classification | Binary/multiclass/multilabel/multioutput on MNIST, Random Forest ROC AUC 0.997 |
+| [linear-models](linear-models/) | Linear models | Normal equation, gradient descent, and regularization implemented from scratch |
+
+Each project folder has its own README with the specific approach, results, and notebook.
+
+## Tech stack
+
+Python, pandas, NumPy, scikit-learn, matplotlib, seaborn
+
+## Author
+
+**Reza** — Computer Networks student, LPIC-1
+[GitHub](https://github.com/rezayaghobi-dev)
diff --git a/projects/notebooks/classification-project/README.md b/projects/notebooks/classification-project/README.md
new file mode 100644
index 0000000..d415b32
--- /dev/null
+++ b/projects/notebooks/classification-project/README.md
@@ -0,0 +1,36 @@
+# MNIST Classification — Binary, Multiclass, Multilabel & Multioutput
+
+A complete tour of classification approaches on the MNIST handwritten digit dataset (70,000 images, 28×28 pixels).
+
+## Overview
+
+Covers four distinct classification problem types on the same dataset, comparing models honestly with cross-validation rather than relying on raw accuracy (which is misleading on imbalanced classes).
+
+## Approach
+
+1. **Binary classification** ("is it an 8?") — SGD Classifier baseline, evaluated against a `DummyClassifier` to prove the model actually learned something beyond guessing the majority class.
+2. **Threshold analysis** — precision-recall and ROC curves to show the tradeoff between precision and recall as the decision threshold moves, instead of reporting one fixed operating point.
+3. **Model comparison** — SGD (linear) vs Random Forest (ensemble) on the same binary task.
+4. **Multiclass classification** — all 10 digits at once, with a 10×10 confusion matrix (normalized by row) to see which digits actually get confused with which.
+5. **Multilabel classification** — predicting multiple independent yes/no labels per sample simultaneously, including `ClassifierChain` to model dependence between labels.
+6. **Multioutput classification** — predicting all 784 pixel values at once (image denoising as a classification problem).
+
+## Results
+
+| Model / Task | Metric | Score |
+|---|---|---|
+| SGD (binary: "is it an 8?") | F1 | 0.58 |
+| SGD (binary) | ROC AUC | 0.915 |
+| Random Forest (binary) | F1 | 0.90 |
+| Random Forest (binary) | ROC AUC | 0.997 |
+| Multilabel (KNN) | Macro F1 | 0.978 |
+
+The SGD → Random Forest jump illustrates why a linear decision boundary struggles on raw pixel data compared to a tree ensemble that can carve non-linear regions.
+
+## Tech stack
+
+Python, scikit-learn, matplotlib
+
+## Notebook
+
+[`notebook.ipynb`](notebook.ipynb)
diff --git a/projects/notebooks/classification-project/classification.ipynb b/projects/notebooks/classification-project/classification.ipynb
new file mode 100644
index 0000000..6a074cd
--- /dev/null
+++ b/projects/notebooks/classification-project/classification.ipynb
@@ -0,0 +1,7407 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "582ba562-7b31-4c83-b514-a5cf2dfd17a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.7.2'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sklearn \n",
+    "sklearn.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5a659a9e-5fab-4331-be88-902fb7178f3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0ab73def-be7f-4e9c-8921-900b4207480c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mnist_dataset = fetch_openml(\"mnist_784\",as_frame=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8119b419-db41-4244-be39-4413cc99d7bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(70000, 784)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mnist_dataset.data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e7308822-a3a7-4a74-a2c1-161df10cc5e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(70000,)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mnist_dataset.target.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "60fd793d-944b-47ae-a744-1a8663652ec2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X,y=mnist_dataset.data,mnist_dataset.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "65dcd884-d874-4c13-9f2a-e20aee7872a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0d2c0457-7479-47f1-aaba-f348957a6a79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0]], shape=(70000, 784))"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "24189808-e5f6-4d17-8b55-45170dc6cbfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x20084c223c0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(X[0].reshape(28,28),cmap=\"binary\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e07be064-20a5-4ba4-9a20-d7fa58e27d4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " X_train ,X_test,y_train, y_test = X[:60000],X[60000:],y[:60000],y[60000:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "68cb596e-79c5-411d-b558-2af754b8788d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import SGDClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8b112e3e-62ce-4250-ad71-c82de1f63ed3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train8 = (y_train==\"8\")\n",
+    "y_test8=(y_test==\"8\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d8c77ff5-04c6-4246-9b5e-62ce646352d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SGDClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.SGDClassifier.html\">?<span>Documentation for SGDClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('loss',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">loss&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;hinge&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">penalty&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
+       "            <td class=\"value\">0.15</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">1000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shuffle',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">shuffle&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('epsilon',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">epsilon&nbsp;</td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;optimal&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('eta0',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">eta0&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('power_t',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">power_t&nbsp;</td>\n",
+       "            <td class=\"value\">0.5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('early_stopping',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">early_stopping&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('average',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">average&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "SGDClassifier()"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgd=SGDClassifier()\n",
+    "sgd.fit(X_train,y_train8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "091c016d-5a98-42c4-81b6-3b37e0f39c27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, ..., False, False,  True], shape=(60000,))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "67178688-6cc1-4fcb-a1e2-b4f04d73b966",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False,  True])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgd.predict(X_train[-3:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ed327992-0cf7-40f0-90da-22fc01b70232",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9427333333333333"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgd.score(X_train,y_train8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d45ffc97-d267-49bc-a5b3-ed175677c789",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from  sklearn.model_selection import cross_val_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b55e7d94-183a-427a-a245-8e7faf723d35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.89041667, 0.9345    , 0.91475   , 0.91      , 0.92958333])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cross_val_score(sgd,X_train,y_train8,cv = 5,scoring=\"accuracy\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "94189627-aba3-4977-910a-8c51bea0c354",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.dummy import DummyClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "76988397-4ba7-4a46-b9aa-a285d5ff7527",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DummyClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DummyClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.dummy.DummyClassifier.html\">?<span>Documentation for DummyClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;prior&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('constant',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">constant&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "DummyClassifier()"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy_c =DummyClassifier()\n",
+    "dummy_c.fit(X_train,y_train8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e8de5ba6-194a-455e-b598-937c0f5470fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy_c.predict(X_train).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bad2d126-5d2c-4d5b-8d8e-0a7140fbc00e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.9025    , 0.9025    , 0.9025    , 0.9025    , 0.90241667])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cross_val_score(dummy_c,X_train,y_train8,cv = 5,scoring=\"accuracy\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5751b85f-f667-48b6-8a3b-876dcf3910f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from  sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.base import clone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "1ba25334-7cf3-42f9-ad01-796ef742711e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "skf = StratifiedKFold(n_splits=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e8113575-c38c-45b9-9703-b18411395b89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9363333333333334\n",
+      "0.92325\n",
+      "0.8845\n",
+      "0.8826666666666667\n",
+      "0.9394166666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "for  train_i , test_i in skf.split(X_train,y_train8):\n",
+    "    cloned_sgd = clone(sgd)\n",
+    "    X_train_f = X_train[train_i]\n",
+    "    y_train8_f = y_train8[train_i]\n",
+    "    X_test_f = X_train[test_i]\n",
+    "    y_test_f = y_train8[test_i]\n",
+    "\n",
+    "    cloned_sgd.fit(X_train_f,y_train8_f)\n",
+    "    y_pred = cloned_sgd.predict(X_test_f)\n",
+    "    correct_predictions = (y_pred == y_test_f).sum()\n",
+    "    print(correct_predictions/len(y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "8346a141-231a-4143-9fda-051d324b49eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import  cross_val_predict "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5360edb3-394c-40fa-bcda-275ff7386fd8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " y_pred= cross_val_predict(sgd,X_train,y_train8,cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d409ee72-27a8-4a3f-a4aa-72184bce396c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "cfb6c459-94bd-4103-b679-b280d063e25f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[51233,  2916],\n",
+       "       [ 1922,  3929]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_train8,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "cb8c924e-e9c2-4385-a84d-3c06121deb08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import precision_score,recall_score,f1_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "fa95e51c-c0a7-4075-9b32-3bc4315f09a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5739956172388605"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(y_train8,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "4be7a682-0d79-493d-a1dc-ab8f72dc6135",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6715091437361135"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(y_train8,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "a3b77e40-fe0c-44ac-8bf6-9fb2936f27d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.618935097668557"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " f1_score(y_train8,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a75cb742-8217-4ad9-a2b5-0066e1e141fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-11247.7289917])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgd.decision_function([X_train[0]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "b9763c0e-aa5a-4761-a315-845c234060bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_scores = cross_val_predict(sgd,X_train,y_train8,cv=5,method=\"decision_function\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3989b9bb-3b18-4d3a-b426-8280a515daec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import precision_recall_curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "55946a23-f577-4439-8957-ccd4583ff311",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prec,recall,thresholds = precision_recall_curve(y_train8,y_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "897cb199-29b2-4fac-bf29-b814628be21b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(60000,)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "thresholds.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "f6d61e56-6f06-43ff-8783-1b8294e3d23f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(60001,)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prec.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "391e51bf-669f-4283-9798-9369461a0031",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(60001,)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "6163651a-604f-44a8-9e42-31969215d050",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b6fd36b0-8c90-4223-acfa-49c6e3c92f05",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2008526b9d0>]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax=plt.subplots()\n",
+    "ax.plot(thresholds,prec[:-1],\"r\",label=\"precision\")\n",
+    "ax.plot(thresholds,recall[:-1],\"black\",label=\"recall\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "4d4ef48a-a34c-455a-9895-b8350e4a02b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'precision')"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(recall,prec)\n",
+    "plt.xlabel(\"recall\")\n",
+    "plt.ylabel(\"precision\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "cb56e2ed-dad8-4297-9435-3f37b94236ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thresh_for_80_prec = thresholds[(prec>= 0.8).argmax()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "c6d7a4cf-7072-4733-8e0e-d7ec7fcc2ab4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_80_prec = (y_scores >= thresh_for_80_prec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "48373946-d4d1-4d14-a58f-4aecf0aacbb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, ..., False, False,  True], shape=(60000,))"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred_80_prec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "99e43b0a-e3f9-4301-b342-5ed4f0ad0159",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(y_train8,y_pred_80_prec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "70e68a41-39ee-4afa-a3ce-18becba777ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.28302854212955053"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(y_train8,y_pred_80_prec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e4afd455-35bb-4a6a-9272-ae12aafa720c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#ROC curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "733a4fef-c1c0-4ed1-a796-02b510ec90d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from  sklearn.metrics import roc_curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "dbe7adca-a1ce-4b14-80cf-38a6cf147c05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fpr,tpr,thresholds = roc_curve(y_train8,y_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "943ec9cd-8410-4d95-96b9-df842b5703b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'TPR')"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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TAEQpwg2iRpvHp4dLPtcvN+zt8t53zsnRolkjNSIr1YTKAADhRLhBRDMMQ6+Vluv59w5o28G6Tu85E+O1ev4kTRrKfDQAEEsIN4hYB2qadNWq93S0wd3lvXvmjNOC6QUmVAUAMBvhBhHnw/21Wvg/W1XT1NZp+8/+4WxdfX6erFb60gBALCPcIGL89oMyLXn1ky7b77xktG6dOcKEigAAfRHhBn1aa7tX//dZlX69eb/e31fb6b2nri1U8VkDTaoMANBXEW7QJ72z66hu/PUWtXl9Xd577J/O0+UTck2oCgAQCQg36FM+OVSv7//qb3K1ejptH5Bq179dOELXTs1nfhoAwEkRbtAnbNlfqxuf36K65vZO2+dOGqwls8cqPTnBpMoAAJGGcANT1Ta16XurNmvP0aZO2//hvEF6ZO4EWmkAAL1GuIFpXvygTEu/Nvpp1T9P1LfH55hUEQAgGhBuEFaGYejlrYd058t/77T96vPztPwfz6alBgBwxgg3CJun39mrB9bt7LL92QXna+aYLBMqAgBEI8INQq607Jj+YcXmTttGZqXox8WjuAUFAAg6wg1CxuczdPmTm/RpuavT9pcWTtP5LGYJAAgRwg1C4tWPDmnx7z7utO2ZBZN04ZhskyoCAMQKq9kFrFixQgUFBXI4HCosLNTGjRtPuv+aNWs0YcIEJSUlKScnR9dff71qamrCVC1O5f8+O6KhS/7YKdjkZyTp8/u/TbABAISFqeFm7dq1WrRokZYtW6bS0lLNmDFDs2fPVllZWbf7b9q0Sdddd51uuOEGbd++XS+99JI+/PBD3XjjjWGuHF9nGIYuf2KTfvDclsC27DS7Xl44TRvunCm7Lc7E6gAAscRiGIZh1pdPmTJFEydO1MqVKwPbxo4dqyuuuELLly/vsv9DDz2klStXas+ePYFtjz/+uB588EEdPHiw2+9wu91yu92B1y6XS3l5eaqvr1daWloQf03s8nh9mv/sB3p395ctaNdOzdd/fmecEmymNw4CAKKAy+WS0+ns0fXbtCtPW1ubtm7dquLi4k7bi4uLtXnz5m6PKSoq0qFDh7Ru3ToZhqEjR47o5Zdf1mWXXXbC71m+fLmcTmfgkZeXF9TfEesO1jZr/D1vdQo22++9RP91xXiCDQDAFKZdfaqrq+X1epWd3bkfRnZ2tiorK7s9pqioSGvWrNG8efOUkJCggQMHql+/fnr88cdP+D1Lly5VfX194HGiFh70jsfr07/+ZqtmPPi2Wtv9K3ffUTxK+5ZfqmQ7/dQBAOYx/X+tvz4jrWEYJ5yldseOHbrtttt01113aevWrXrzzTe1b98+LVy48ISfb7fblZaW1umBM3P1U+9pxLI/6U+ffhlC75kzTj+8cCQzDAMATGfa/2JnZmYqLi6uSytNVVVVl9acDsuXL9f06dN15513SpLOOeccJScna8aMGbr//vuVk8OEcKHU6PbonHveku8rvbSGD0jWizdNVVaaw7zCAAD4CtNabhISElRYWKiSkpJO20tKSlRUVNTtMc3NzbJaO5ccF+cfhWNiv+iYYBiGxt/dOdi88q/T9Jcff4tgAwDoU0ztHLF48WJde+21mjRpkqZNm6annnpKZWVlgdtMS5cuVXl5uZ5//nlJ0pw5c3TTTTdp5cqVuuSSS1RRUaFFixZp8uTJys3NNfOnRDXDMFSwdF3g9TdHDdDzP5hsYkUAAJyYqeFm3rx5qqmp0X333aeKigqNHz9e69atU35+viSpoqKi05w3CxYsUENDg5544gn9+Mc/Vr9+/XThhRfq5z//uVk/IeoZhqFrnn4/8Hr+tHzd+93xJlYEAMDJmTrPjRl6M04+1u2vbtK3Hvpr4PV5Q/rptVumm1cQACBmRcQ8N+jbmtyeTsFmbE6aXrxpqnkFAQDQQ0xIgi4Mw9CcxzcFXv9w5gjdccloEysCAKDnCDfo4oE/7tTe6iZJ0rJLx+qmbw4zuSIAAHqO21Lo5KUtB/WrTfskSTNHDyDYAAAiDi03CJj1yAbtrmqUJF0waoCeWXC+yRUBANB7tNxAkjR31XuBYBMfZ9GzC85nKQUAQEQi3EAvflCmD/bXBl5/fHexrFaCDQAgMnFbKsb95m8H9B+vfxp4vednlyqOYAMAiGC03MSwAzVNnYLNR/95McEGABDxaLmJUW6PV5c+ujHwuvQ/L1b/5AQTKwIAIDhouYlR//WHHWpq80qSXr2liGADAIgahJsY9Gl5vX7zN/+CpJeePVATh/Q3uSIAAIKHcBNjDte16DtfWVrhkbnnmlcMAAAhQLiJIYZh6LLHvuxn87ubp8kRH2diRQAABB/hJoY8tP5zHWtulyT9Yt65mlyQbnJFAAAEH+EmRvxtb42efHuPJCkjOUFXnDfI5IoAAAgNwk0MMAxDt6z5KPD6D7d9w8RqAAAILcJNlDMMQ+c/8BfVNrVJkn77L1OV40w0uSoAAEKHcBPlVm3Yq+pGtyRpQdFQTR2WYXJFAACEFuEmir21vVI/f/MzSdKgfom65/KzTK4IAIDQI9xEqeY2j27+n62B17/9l6kmVgMAQPgQbqJQk9ujcXe9FXi97rYZyktPMrEiAADCh3AThW57sTTw/N+/PUbjctNMrAYAgPAi3ESZ8roW/eWzKknS1efn6V+/NdzkigAACC/CTZT52bqdkqQcp0PL//Fsk6sBACD8CDdR5JND9frj3yskSbddNFIWi8XkigAACD/CTRRZtNbf1+a8If109fl5JlcDAIA5CDdRYv32Su052iRJuv+K8bTaAABiFuEmSjz41ueSpBkjM3VWrtPkagAAMA/hJgrsOOzS7qpGSf4lFgAAiGWEmyhw1arNkqRUu00Xjc02uRoAAMxFuIlwu6sa1dzmlSTdcclok6sBAMB8hJsIN+uRDZKkVIdN87klBQAA4SaSHXG1Bp7f/M1hJlYCAEDfQbiJYM++uz/w/NaZI8wrBACAPoRwE6EMw9CqDXskSQ/8A/PaAADQgXAToX7ztwOB598cOcDESgAA6FsINxHqf46Hm7MHOZWXnmRyNQAA9B2Emwh0sLZZu474J+1b8f2JJlcDAEDfQriJQG98fFiSdM5gWm0AAPg6wk0E+u2HZZKky87OMbkSAAD6HsJNhKlrbtPB2hZJ0rThGSZXAwBA30O4iTCrNuwNPD97EKt/AwDwdYSbCPPiB/5bUv/yzWHMbQMAQDcINxGkor5F9S3tkqS5k/JMrgYAgL6JcBNBfvvBQUnSmIGpGpGVYnI1AAD0TYSbCPLKR4ckSd85h1FSAACcCOEmQni8Ph065h8lNah/osnVAADQdxFuIsSfd1YFns8eT8sNAAAnQriJED997RNJ0pwJuXLEx5lcDQAAfRfhJgLUNbeptqlNkjRrbJbJ1QAA0LcRbiLAf/7v9sDz7547yMRKAADo+wg3EaCstlmSlJGcYHIlAAD0fYSbPq7K1aqPD9ZJkp66bpK5xQAAEAEIN33c/33mHyU1MitFhfn9Ta4GAIC+j3DTxy151T9KqogVwAEA6BHTw82KFStUUFAgh8OhwsJCbdy48aT7u91uLVu2TPn5+bLb7Ro+fLieeeaZMFUbXs1tnsDzs3JZARwAgJ6wmfnla9eu1aJFi7RixQpNnz5dv/zlLzV79mzt2LFDQ4YM6faYuXPn6siRI1q9erVGjBihqqoqeTyebveNdE+/sy/w/B8nMkoKAICesBiGYZj15VOmTNHEiRO1cuXKwLaxY8fqiiuu0PLly7vs/+abb+rqq6/W3r17lZ6e3qPvcLvdcrvdgdcul0t5eXmqr69XWlramf+IELrssY3aftil0dmpeuv2b5pdDgAApnG5XHI6nT26fpt2W6qtrU1bt25VcXFxp+3FxcXavHlzt8e88cYbmjRpkh588EENGjRIo0aN0h133KGWlpYTfs/y5cvldDoDj7y8vKD+jlBpbfdq+2GXJOnhuRNMrgYAgMhh2m2p6upqeb1eZWdnd9qenZ2tysrKbo/Zu3evNm3aJIfDoddee03V1dW65ZZbVFtbe8J+N0uXLtXixYsDrztabvq6Nz/98hycldu3W5gAAOhLTO1zI0kWi6XTa8Mwumzr4PP5ZLFYtGbNGjmd/g62jzzyiK666io9+eSTSkzsulq23W6X3W4PfuEh9re9NZKk0dmpJzwfAACgK9NuS2VmZiouLq5LK01VVVWX1pwOOTk5GjRoUCDYSP4+OoZh6NChQyGtN9x+++FBSdJtF400uRIAACKLaeEmISFBhYWFKikp6bS9pKRERUVF3R4zffp0HT58WI2NjYFtu3btktVq1eDBg0Nabzi1e32B5yOzU0ysBACAyGPqPDeLFy/Wr371Kz3zzDPauXOnbr/9dpWVlWnhwoWS/P1lrrvuusD+11xzjTIyMnT99ddrx44deuedd3TnnXfqBz/4Qbe3pCLVH/5+OPB8xADCDQAAvWFqn5t58+appqZG9913nyoqKjR+/HitW7dO+fn5kqSKigqVlZUF9k9JSVFJSYn+7d/+TZMmTVJGRobmzp2r+++/36yfEBKrN/nnt/le4WBZrfS3AQCgN0yd58YMvRknb4ajDW6d/8CfJUnrbpuhcYyUAgAgMua5QffePr5Q5tCMJIINAACngXDTx7xWWi5JykyJvOHrAAD0BYSbPuZATZMk6bvn5ppcCQAAkYlw04c0uT06XN8qSSoakWlyNQAARCbCTR/y6kf+iQgHpjk0LDPZ5GoAAIhMhJs+ZMOuo5Kkifn9WHIBAIDTRLjpQ/680z9SasxARkkBAHC6CDd9RH1Le+D5xeO6X1sLAACcGuGmj3jm+KzEkjRmYKqJlQAAENkIN33E77b4VwG/cEwW/W0AADgDhJs+YHdVoyqODwG/85LRJlcDAEBkI9z0Ab/9wL846KB+iRqbQ2diAADOBOGmD2hu90qSzhnsNLkSAAAiH+GmD/jiSIMk6VujB5hcCQAAkS+o4ebDDz8M5sfFBMMwtLPCH26Y3wYAgDPX63DT2NiolpaWTtu2bdumOXPmaOrUqUErLFZsP+xSo9sjq0X0twEAIAh6HG4OHTqk6dOny+l0yul0avHixWpubtZ1112n888/X3a7XZs2bQplrVHpr5/7ZyX2GVKCjbuEAACcKVtPd1yyZIkaGxv16KOP6pVXXtGjjz6qDRs2aMKECdq1a5cKCgpCWWfUKtlxRJI0bAALZQIAEAw9Djdvv/22fve732n69Om66qqrlJubq+9973tasmRJKOuLen8vr5ckzRydZXIlAABEhx7fB6msrNTw4cMlSQMHDlRiYqK++93vhqywWGAYhgzD/5yRUgAABEevOnnExcV9eaDVKofDEfSCYomrxRN4fm5eP/MKAQAgivT4tpRhGLroootks/kPaWlp0Zw5c5SQkNBpv48++ii4FUax8rovR52lOuJNrAQAgOjR43Bz9913d3rNLakz91mlS5I0gZmJAQAImtMONzhzb22vlCSNzE41uRIAAKJHj8ONJL3//vt644031N7erlmzZqm4uDhUdcWE+pZ2SVJBJsPAAQAIlh6Hm9dee03f+9735HA4ZLPZ9PDDD+vhhx/WokWLQlhedPvb3lpJ0vQRmSZXAgBA9OjxaKmf/exnWrBggerq6lRXV6d7771X999/fyhri2rVje7A89HclgIAIGh6HG4+//xz/eQnPwmMlrrzzjtVV1en6urqkBUXzfZVNwWeJybEnWRPAADQGz0ON42NjerXr1/gtd1uV2JiolwuVyjqinrv762RJI3KTjG5EgAAokuvOhS/9dZbcjq/HLbs8/n0l7/8RZ9++mlg2+WXXx686qJYdWObJP+CmQAAIHh6FW7mz5/fZdvNN98ceG6xWOT1es+8qhiwo8Lf4nXjN1hwFACAYOpxuPH5fKGsI+Z8sM8/Ump4FrelAAAIph73ufnBD36ghoaGUNYSM5rcX64pxRw3AAAEV4/Dza9//Wu1tLScekec0ldHSmWm2E2sBACA6NPjcGMY9HwNlg27jkqSJrASOAAAQdfjcCP5OwzjzG3e458bKDuVVhsAAIKtV6OlRo0adcqAU1tbe0YFxYKOvtmF+f3NLQQAgCjUq3Bz7733dprnBqenrLZZknT2IM4lAADB1qtwc/XVVysrKytUtcQEj9en8jp/x+x8RkoBABB0Pe5zQ3+b4Kiobw08z0lzmFgJAADRidFSYXbomL/Vpl9SvKxWAiMAAMHGDMVhtre6UZJ0zuB+5hYCAECU6tVQcJy53VX+cDOM/jYAAIQE4SbMtpf7F8wcm5NqciUAAEQnwk2Y7aryr891Vi7DwAEACAXCTRg1tLarrrldkpSfkWRyNQAARCfCTRh19LdJT05QqiPe5GoAAIhOhJswOuLyz3EzJJ1WGwAAQoVwE0Ydyy4M6p9ociUAAEQvwk0YlR+fwG9wP8INAAChQrgJo427qyVJOU6WXQAAIFQIN2G092iTJMkeH2dyJQAARC/CjQmGD0gxuwQAAKIW4SZMmtyewPNR2YQbAABChXATJh0jpVLsNvVLSjC5GgAAohfhJkw+KjsmSRrMMHAAAELK9HCzYsUKFRQUyOFwqLCwUBs3buzRce+++65sNpvOPffc0BYYJB2zE3t9hsmVAAAQ3UwNN2vXrtWiRYu0bNkylZaWasaMGZo9e7bKyspOelx9fb2uu+46XXTRRWGq9Mz5joeagsxkkysBACC6mRpuHnnkEd1www268cYbNXbsWP3iF79QXl6eVq5cedLjbr75Zl1zzTWaNm1amCo9c3/5rEqS9I2RmSZXAgBAdDMt3LS1tWnr1q0qLi7utL24uFibN28+4XHPPvus9uzZo7vvvrtH3+N2u+VyuTo9zNCxUGZygs2U7wcAIFaYFm6qq6vl9XqVnZ3daXt2drYqKyu7PeaLL77QkiVLtGbNGtlsPQsJy5cvl9PpDDzy8vLOuPbTcbjOv/TCuNw0U74fAIBYYXqHYovF0um1YRhdtkmS1+vVNddco3vvvVejRo3q8ecvXbpU9fX1gcfBgwfPuObeamnzqr6lXZKU62S0FAAAoWTaPZLMzEzFxcV1aaWpqqrq0pojSQ0NDdqyZYtKS0v1wx/+UJLk8/lkGIZsNpvWr1+vCy+8sMtxdrtddrs9ND+ihypdrYHnaYnclgIAIJRMa7lJSEhQYWGhSkpKOm0vKSlRUVFRl/3T0tL0ySefaNu2bYHHwoULNXr0aG3btk1TpkwJV+m9VnH8lpTUtaUKAAAEl6nNCIsXL9a1116rSZMmadq0aXrqqadUVlamhQsXSvLfUiovL9fzzz8vq9Wq8ePHdzo+KytLDoejy/a+pu74LalEFswEACDkTA038+bNU01Nje677z5VVFRo/PjxWrdunfLz8yVJFRUVp5zzJhLsq/avBj5rXNfbbQAAILgshmHE1JS5LpdLTqdT9fX1SksLz8ilG3/9of68s0q3fGu4fvLtMWH5TgAAoklvrt+mj5aKBR3xMc5KfxsAAEKNcBMG2w/7Jw4cmsHSCwAAhBrhJgw6hoIXDCDcAAAQaoSbEPN4fYHnTOAHAEDoEW5CrLqxLfB8QKq5kwkCABALCDchdrTBHXhOh2IAAEKPcBNiOyv8nYnPHuQ0uRIAAGID4SbEGtweSZIvtqYTAgDANISbEPu80t9yk0NnYgAAwoJwE2L1x9eVorsNAADhQbgJMYv8qWbYgBSTKwEAIDYQbkKstsk/FHxIepLJlQAAEBsINyH2wf5aSdLQTMINAADhQLgJk8wUJvADACAcCDchVFnfGnjObSkAAMKDcBNCe442SpJS7TY54uNMrgYAgNhAuAmh8roWSdJ4ZicGACBsCDchtLvK33LjTIw3uRIAAGIH4SaEth44Jklq9XhNrgQAgNhBuAmhpAR/P5scp8PkSgAAiB2EmxDqWCxzXE6ayZUAABA7CDchVNPon52YRTMBAAgfwk0IuY4vmpmRkmByJQAAxA7CTQjVHQ83jJYCACB8CDch0ubxqbnNP0oqPZmWGwAAwoVwEyJ1LW2B5yl2m4mVAAAQWwg3IXLoWEvguS2O0wwAQLhw1Q2R6ga3JIaBAwAQboSbEGl0eyRJ/ZPpTAwAQDgRbkKktsnf5yY92W5yJQAAxBbCTYgcPX5bKj2JlhsAAMKJcBMi9cfnuBmQSssNAADhRLgJkY5wk8YEfgAAhBXhJkSONfv73DA7MQAA4UW4CZGORTP7JTE7MQAA4US4CZEvqholSRksvQAAQFgRbkLEEW89/mecyZUAABBbCDchYBiGWtt9kqS0RNaVAgAgnAg3IdCxGrgkJScQbgAACCfCTQg0tHoCz5MSuC0FAEA4EW5CoGMYuN1mlcViMbkaAABiC+EmBNweX6c/AQBA+BBuQuCIq1WSNDIrxeRKAACIPYSbEGj3+ltsDh1rMbkSAABiD+EmBOqa/etKTRueYXIlAADEHsJNCHSMlurP0gsAAIQd4SYEGt3+lpvEBE4vAADhxtU3BL444l9Xymbl9AIAEG5cfUMgM9UuSWp0e06xJwAACDbCTQiUHx8lxVBwAADCj3ATAvUt/j43TE4MAED4EW5CwDAMSZIjnnWlAAAIN8JNCByobZYkDUixm1wJAACxh3ATAknHW2xouQEAIPwINyFwuN6/tlRGCpP4AQAQboSbEOqXSLgBACDcTA83K1asUEFBgRwOhwoLC7Vx48YT7vvqq6/q4osv1oABA5SWlqZp06bprbfeCmO1p9b0lblt+ifHm1gJAACxydRws3btWi1atEjLli1TaWmpZsyYodmzZ6usrKzb/d955x1dfPHFWrdunbZu3aqZM2dqzpw5Ki0tDXPlJ+ZqbQ88T7HbTKwEAIDYZDE6xi2bYMqUKZo4caJWrlwZ2DZ27FhdccUVWr58eY8+46yzztK8efN011139Wh/l8slp9Op+vp6paWlnVbdJ/Npeb2+8/gm9UuK17a7ioP++QAAxKLeXL9Na7lpa2vT1q1bVVzcOQAUFxdr8+bNPfoMn8+nhoYGpaenn3Aft9stl8vV6RFK5XX+2YkzGQYOAIApTAs31dXV8nq9ys7O7rQ9OztblZWVPfqMhx9+WE1NTZo7d+4J91m+fLmcTmfgkZeXd0Z1n0pHQ1hZTXNIvwcAAHTP9A7Flq+tUWAYRpdt3XnxxRd1zz33aO3atcrKyjrhfkuXLlV9fX3gcfDgwTOu+WRqmtokSVOGnbg1CQAAhI5pPV4zMzMVFxfXpZWmqqqqS2vO161du1Y33HCDXnrpJc2aNeuk+9rtdtnt4btFVNfs71Ds8ZrWlQkAgJhmWstNQkKCCgsLVVJS0ml7SUmJioqKTnjciy++qAULFuiFF17QZZddFuoye63x+FDwOCurZgIAYAZTxyovXrxY1157rSZNmqRp06bpqaeeUllZmRYuXCjJf0upvLxczz//vCR/sLnuuuv06KOPaurUqYFWn8TERDmdTtN+x1cdO35bKrefw+RKAACITaaGm3nz5qmmpkb33XefKioqNH78eK1bt075+fmSpIqKik5z3vzyl7+Ux+PRrbfeqltvvTWwff78+XruuefCXX63OvrcWHvQbwgAAASfqfPcmCHU89zMf+YDbdh1VLddOEKLi0cH/fMBAIhFETHPTbTq6GvjTGJdKQAAzEC4CbLmNn+H4gGpTOIHAIAZCDdB1jEUvH8Si2YCAGAGwk2QVTf6OxT357YUAACmINwEWWu7V5KUzIrgAACYgnATZG0enyTJbuPUAgBgBq7AQWQYhtq8/nCTQLgBAMAUXIGDyH281UYi3AAAYBauwEHU0WojcVsKAACzcAUOovavtNzEWzm1AACYgStwEHW03NisFllZFRwAAFMQboKouc0/DJxbUgAAmIercBC52/0tN0nMcQMAgGkIN0EUGAYex2kFAMAsXIWDqJ05bgAAMB1X4SCqOb6ulGEYJlcCAEDsItwEUdzxEVJHXG6TKwEAIHYRboKouc0jSTpnsNPkSgAAiF2EmyA6XNcq6csWHAAAEH6EmyBKscdJkqoauC0FAIBZCDdB1LFw5lm5aSZXAgBA7CLcBNGBmmZJzFAMAICZuAoHUfLxmYkrGS0FAIBpCDchkNc/0ewSAACIWYSbIPL6/H1uUlhbCgAA0xBugqjd65+Z2BbHUHAAAMxCuAkiz/GWmzgrpxUAALNwFQ6ivx+qlyTFM4kfAACmIdwE0ZD0JElSdSOjpQAAMAvhJog8x/vcjMhONbkSAABiF+EmiDr63HBbCgAA8xBugqhjtBQLZwIAYB7CTRB9fKhOEkPBAQAwE+EmiEZl+fvaNLq9JlcCAEDsItwEUUefm6xUu8mVAAAQuwg3QeT1+fvcxHNbCgAA0xBugujLDsWcVgAAzMJVOIgCLTeMlgIAwDSEmyD6cm0pwg0AAGYh3ASR2+MPN/b4OJMrAQAgdhFugqjjtpSNlhsAAExDuAkij48ZigEAMBvhJohouQEAwHyEmyDqCDdWwg0AAKYh3AQRLTcAAJiPcBNEbo9/TSmrhXADAIBZCDdB1DFDMbelAAAwD+EmiDoabJihGAAA8xBugsQwDBn+hhuGggMAYCLCTZB0BBuJPjcAAJiJcBMkvq+kG8INAADmIdwEie8rLTcWzioAAKbhMhwktNwAANA3EG6C5KvhJo5wAwCAaQg3QdLpthTZBgAA0xBugoTbUgAA9A2mh5sVK1aooKBADodDhYWF2rhx40n337BhgwoLC+VwODRs2DCtWrUqTJWenOH78jnT3AAAYB5Tw83atWu1aNEiLVu2TKWlpZoxY4Zmz56tsrKybvfft2+fLr30Us2YMUOlpaX66U9/qttuu02vvPJKmCvvipYbAAD6BothfHX6ufCaMmWKJk6cqJUrVwa2jR07VldccYWWL1/eZf9///d/1xtvvKGdO3cGti1cuFAff/yx3nvvvR59p8vlktPpVH19vdLS0s78RxxX0+hW4f1/liTtW36pLAQcAACCpjfXb9Nabtra2rR161YVFxd32l5cXKzNmzd3e8x7773XZf9LLrlEW7ZsUXt7e7fHuN1uuVyuTo9Q6OhQbLGIYAMAgIlMCzfV1dXyer3Kzs7utD07O1uVlZXdHlNZWdnt/h6PR9XV1d0es3z5cjmdzsAjLy8vOD/gawwZcsRb5bDFheTzAQBAz5jeofjrrRyGYZy05aO7/bvb3mHp0qWqr68PPA4ePHiGFXcvK9Whz/5rtnb+17dD8vkAAKBnbGZ9cWZmpuLi4rq00lRVVXVpnekwcODAbve32WzKyMjo9hi73S673R6cogEAQJ9nWstNQkKCCgsLVVJS0ml7SUmJioqKuj1m2rRpXfZfv369Jk2apPj4+JDVCgAAIoept6UWL16sX/3qV3rmmWe0c+dO3X777SorK9PChQsl+W8pXXfddYH9Fy5cqAMHDmjx4sXauXOnnnnmGa1evVp33HGHWT8BAAD0MabdlpKkefPmqaamRvfdd58qKio0fvx4rVu3Tvn5+ZKkioqKTnPeFBQUaN26dbr99tv15JNPKjc3V4899piuvPJKs34CAADoY0yd58YMoZrnBgAAhE5EzHMDAAAQCoQbAAAQVQg3AAAgqhBuAABAVCHcAACAqEK4AQAAUYVwAwAAogrhBgAARBXCDQAAiCqmLr9gho4JmV0ul8mVAACAnuq4bvdkYYWYCzcNDQ2SpLy8PJMrAQAAvdXQ0CCn03nSfWJubSmfz6fDhw8rNTVVFoslqJ/tcrmUl5engwcPsm5VCHGew4PzHB6c5/DhXIdHqM6zYRhqaGhQbm6urNaT96qJuZYbq9WqwYMHh/Q70tLS+A8nDDjP4cF5Dg/Oc/hwrsMjFOf5VC02HehQDAAAogrhBgAARBXCTRDZ7XbdfffdstvtZpcS1TjP4cF5Dg/Oc/hwrsOjL5znmOtQDAAAohstNwAAIKoQbgAAQFQh3AAAgKhCuAEAAFGFcNNLK1asUEFBgRwOhwoLC7Vx48aT7r9hwwYVFhbK4XBo2LBhWrVqVZgqjWy9Oc+vvvqqLr74Yg0YMEBpaWmaNm2a3nrrrTBWG7l6+/e5w7vvviubzaZzzz03tAVGid6eZ7fbrWXLlik/P192u13Dhw/XM888E6ZqI1dvz/OaNWs0YcIEJSUlKScnR9dff71qamrCVG1keueddzRnzhzl5ubKYrHo9ddfP+UxplwHDfTYb3/7WyM+Pt54+umnjR07dhg/+tGPjOTkZOPAgQPd7r93714jKSnJ+NGPfmTs2LHDePrpp434+Hjj5ZdfDnPlkaW35/lHP/qR8fOf/9z44IMPjF27dhlLly414uPjjY8++ijMlUeW3p7nDnV1dcawYcOM4uJiY8KECeEpNoKdznm+/PLLjSlTphglJSXGvn37jPfff9949913w1h15Onted64caNhtVqNRx991Ni7d6+xceNG46yzzjKuuOKKMFceWdatW2csW7bMeOWVVwxJxmuvvXbS/c26DhJuemHy5MnGwoULO20bM2aMsWTJkm73/8lPfmKMGTOm07abb77ZmDp1ashqjAa9Pc/dGTdunHHvvfcGu7Socrrned68ecZ//Md/GHfffTfhpgd6e57/9Kc/GU6n06ipqQlHeVGjt+f5v//7v41hw4Z12vbYY48ZgwcPDlmN0aYn4cas6yC3pXqora1NW7duVXFxcaftxcXF2rx5c7fHvPfee132v+SSS7Rlyxa1t7eHrNZIdjrn+et8Pp8aGhqUnp4eihKjwume52effVZ79uzR3XffHeoSo8LpnOc33nhDkyZN0oMPPqhBgwZp1KhRuuOOO9TS0hKOkiPS6ZznoqIiHTp0SOvWrZNhGDpy5IhefvllXXbZZeEoOWaYdR2MuYUzT1d1dbW8Xq+ys7M7bc/OzlZlZWW3x1RWVna7v8fjUXV1tXJyckJWb6Q6nfP8dQ8//LCampo0d+7cUJQYFU7nPH/xxRdasmSJNm7cKJuNfzp64nTO8969e7Vp0yY5HA699tprqq6u1i233KLa2lr63ZzA6ZznoqIirVmzRvPmzVNra6s8Ho8uv/xyPf744+EoOWaYdR2k5aaXLBZLp9eGYXTZdqr9u9uOznp7nju8+OKLuueee7R27VplZWWFqryo0dPz7PV6dc011+jee+/VqFGjwlVe1OjN32efzyeLxaI1a9Zo8uTJuvTSS/XII4/oueeeo/XmFHpznnfs2KHbbrtNd911l7Zu3ao333xT+/bt08KFC8NRakwx4zrI/371UGZmpuLi4rr8X0BVVVWXVNph4MCB3e5vs9mUkZERsloj2emc5w5r167VDTfcoJdeekmzZs0KZZkRr7fnuaGhQVu2bFFpaal++MMfSvJfhA3DkM1m0/r163XhhReGpfZIcjp/n3NycjRo0CA5nc7AtrFjx8owDB06dEgjR44Mac2R6HTO8/LlyzV9+nTdeeedkqRzzjlHycnJmjFjhu6//35a1oPErOsgLTc9lJCQoMLCQpWUlHTaXlJSoqKiom6PmTZtWpf9169fr0mTJik+Pj5ktUay0znPkr/FZsGCBXrhhRe4Z94DvT3PaWlp+uSTT7Rt27bAY+HChRo9erS2bdumKVOmhKv0iHI6f5+nT5+uw4cPq7GxMbBt165dslqtGjx4cEjrjVSnc56bm5tltXa+BMbFxUn6smUBZ86062BIuytHmY6hhqtXrzZ27NhhLFq0yEhOTjb2799vGIZhLFmyxLj22msD+3cMgbv99tuNHTt2GKtXr2YoeA/09jy/8MILhs1mM5588kmjoqIi8KirqzPrJ0SE3p7nr2O0VM/09jw3NDQYgwcPNq666ipj+/btxoYNG4yRI0caN954o1k/ISL09jw/++yzhs1mM1asWGHs2bPH2LRpkzFp0iRj8uTJZv2EiNDQ0GCUlpYapaWlhiTjkUceMUpLSwND7vvKdZBw00tPPvmkkZ+fbyQkJBgTJ040NmzYEHhv/vz5xgUXXNBp/7/+9a/GeeedZyQkJBhDhw41Vq5cGeaKI1NvzvMFF1xgSOrymD9/fvgLjzC9/fv8VYSbnuvted65c6cxa9YsIzEx0Rg8eLCxePFio7m5OcxVR57enufHHnvMGDdunJGYmGjk5OQY3//+941Dhw6FuerI8vbbb5/039u+ch20GAbtbwAAIHrQ5wYAAEQVwg0AAIgqhBsAABBVCDcAACCqEG4AAEBUIdwAAICoQrgBAABRhXADAACiCuEGAABEFcINgD5vwYIFslgsXR67d+/u9F58fLyGDRumO+64Q01NTZKk/fv3dzrG6XRq6tSp+v3vf2/yrwIQKoQbABHh29/+tioqKjo9CgoKOr23d+9e3X///VqxYoXuuOOOTsf/+c9/VkVFhd5//31NnjxZV155pT799FMzfgqAECPcAIgIdrtdAwcO7PSIi4vr9F5eXp6uueYaff/739frr7/e6fiMjAwNHDhQY8aM0QMPPKD29na9/fbbJvwSAKFGuAEQdRITE9Xe3t7te+3t7Xr66aclSfHx8eEsC0CY2MwuAAB64g9/+INSUlICr2fPnq2XXnqpy34ffPCBXnjhBV100UWdthcVFclqtaqlpUU+n09Dhw7V3LlzQ143gPAj3ACICDNnztTKlSsDr5OTkwPPO4KPx+NRe3u7vvvd7+rxxx/vdPzatWs1ZswY7dq1S4sWLdKqVauUnp4etvoBhA/hBkBESE5O1ogRI7p9ryP4xMfHKzc3t9vbTXl5eRo5cqRGjhyplJQUXXnlldqxY4eysrJCXTqAMKPPDYCI1xF88vPze9SP5oILLtD48eP1wAMPhKE6AOFGuAEQk3784x/rl7/8pcrLy80uBUCQEW4AxKTvfOc7Gjp0KK03QBSyGIZhmF0EAABAsNByAwAAogrhBgAARBXCDQAAiCqEGwAAEFUINwAAIKoQbgAAQFQh3AAAgKhCuAEAAFGFcAMAAKIK4QYAAEQVwg0AAIgq/x8qiaamGsyyLAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(fpr,tpr,)\n",
+    "plt.xlabel(\"FPR\")\n",
+    "plt.ylabel(\"TPR\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "51554cdd-f12a-42bf-a005-ae02930cc3dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from  sklearn.metrics import roc_auc_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "ac1a530b-99fc-4fa4-b193-201a470db31f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.902325514217357"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roc_auc_score(y_train8,y_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c923bfe-8d3c-45ae-a17c-c002474012e3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "702c4423",
+   "metadata": {},
+   "source": [
+    "# Random Forest version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "03511b3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "e33d2d9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">criterion&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_depth&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_features&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">oob_score&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_samples&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier()"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf=RandomForestClassifier()\n",
+    "rf.fit(X_train,y_train8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "295bf43c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False,  True])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf.predict(X_train[-3:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "b90b85b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf.score(X_train,y_train8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "f2ed5b0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.98141667, 0.98183333, 0.982     , 0.97941667, 0.9835    ])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cross_val_score(rf,X_train,y_train8,cv = 5,scoring=\"accuracy\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "3b2241b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_rf= cross_val_predict(rf,X_train,y_train8,cv=5)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "97b43edd-bbbb-40ab-ac5f-ecf5bcd944ce",
+   "metadata": {},
+   "source": [
+    "confusion_matrix(y_train8,y_pred_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "b2db46d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9880510918829831"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(y_train8,y_pred_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "364475a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8196889420611861"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(y_train8,y_pred_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "0f873b15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8960298925735638"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_train8,y_pred_rf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "151fe8ce",
+   "metadata": {},
+   "source": [
+    "random forest doesnt have decision_function so we use predict_proba instead"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "d099f79d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(60000, 2)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_probas_rf = cross_val_predict(rf,X_train,y_train8,cv=5,method=\"predict_proba\")\n",
+    "y_probas_rf.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "3ce28b86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_scores_rf = y_probas_rf[:,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "8c42922e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prec_rf,recall_rf,thresholds_rf = precision_recall_curve(y_train8,y_scores_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "95da21c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'precision')"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(recall_rf,prec_rf)\n",
+    "plt.xlabel(\"recall\")\n",
+    "plt.ylabel(\"precision\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "64465baf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fpr_rf,tpr_rf,thresholds_rf = roc_curve(y_train8,y_scores_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "4b35db9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'TPR')"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(fpr_rf,tpr_rf,)\n",
+    "plt.xlabel(\"FPR\")\n",
+    "plt.ylabel(\"TPR\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "fb459479",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.996934364237175"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roc_auc_score(y_train8,y_scores_rf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "558afe22",
+   "metadata": {},
+   "source": [
+    "## 11. Bonus — compare both models on one ROC plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "b1dbf62d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'ROC Comparison')"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(fpr,tpr,\"b:\",label=\"SGD\")\n",
+    "plt.plot(fpr_rf,tpr_rf,\"r-\",label=\"Random Forest\")\n",
+    "plt.xlabel(\"FPR\")\n",
+    "plt.ylabel(\"TPR\")\n",
+    "plt.legend()\n",
+    "plt.title(\"ROC Comparison\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1077275-0c66-4c13-a253-a39da8aa13b0",
+   "metadata": {},
+   "source": [
+    "# classfication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "0a0dfeeb-366f-4aff-a6df-d006689b63cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OvO -> n*(n-1)/2\n",
+    "# OvR - OvA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "0e62cd31-fd5f-4071-ac61-6639fd2eaab6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVC\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "6d62109b-fbb5-4a90-855f-9e15b96bdae4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kernel',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kernel&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('degree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">degree&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">gamma&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('coef0',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">coef0&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shrinking',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">shrinking&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('probability',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">probability&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cache_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cache_size&nbsp;</td>\n",
+       "            <td class=\"value\">200</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">-1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('decision_function_shape',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">decision_function_shape&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('break_ties',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">break_ties&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "SVC()"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc = SVC()\n",
+    "svc.fit(X_train[:1000],y_train[:1000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "e8cd5a90-61f1-4da4-9023-0f9ae2b5571d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['5', '0', '4', ..., '5', '6', '8'], shape=(60000,), dtype=object)"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "768d3a7a-1e84-4a84-a7c3-52e590e9d9f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['5'], dtype=object)"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc.predict([X_train[0]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "7723dd2f-108d-466e-b1b6-11043da14388",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.75828215,  2.74956232,  6.13809559,  8.2853702 , -0.28728967,\n",
+       "         9.30119996,  0.74228825,  3.79256174,  7.20847395,  4.85762716]])"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc.decision_function([X_train[0]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "57b39339-dbed-450d-813f-3167d98bd82f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], dtype=object)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc.classes_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "f508db7f-53a7-4276-acc4-256d957464b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.multiclass import OneVsRestClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "3a7fa3a4-1767-43a2-bd70-d8c4eb9863cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ovr_svm  = OneVsRestClassifier(SVC())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "7be8d6cc-4f9d-4732-b7c8-e53a62c70813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=SVC())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsRestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">estimator&nbsp;</td>\n",
+       "            <td class=\"value\">SVC()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: SVC</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kernel',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kernel&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('degree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">degree&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">gamma&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('coef0',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">coef0&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shrinking',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">shrinking&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('probability',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">probability&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cache_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cache_size&nbsp;</td>\n",
+       "            <td class=\"value\">200</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">-1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('decision_function_shape',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">decision_function_shape&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('break_ties',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">break_ties&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "OneVsRestClassifier(estimator=SVC())"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ovr_svm.fit(X_train[:1000],y_train[:1000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "bc2f7f3a-d63e-4a61-9a17-0bd4f48352a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['5'], dtype='<U1')"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ovr_svm.predict([X_train[0]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "56fa3773-e4d5-4373-83d0-9afac7fdf020",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import ConfusionMatrixDisplay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "a5c6833a-7eea-4f2d-89a0-0c2e8a94b1fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = cross_val_predict(svc,X_train[:1000],y_train[:1000],cv=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "a30fc5ef-06fa-4c0b-b18b-84feec19fef6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x20085642660>"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ConfusionMatrixDisplay.from_predictions(y_train[:1000],y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "44253720-3663-4682-9b5a-21e1151a589c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x20086428690>"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ConfusionMatrixDisplay.from_predictions(y_train[:1000],y_pred,normalize=\"true\",values_format=\".0%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "fbb42587-9422-4f57-a8f7-c610f1580410",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weights = (y_pred != y_train[:1000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "b0763a36-b853-4f25-bac0-9e590e937dac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x200865aa0d0>"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ConfusionMatrixDisplay.from_predictions(y_train[:1000],y_pred,sample_weight=weights,normalize=\"true\",values_format=\".0%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24e2d595-04ce-4126-b491-e57e5d60c954",
+   "metadata": {},
+   "source": [
+    "# multilable classification "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "f72317aa-9e64-4871-95d0-6e5942b2535e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors  import KNeighborsClassifier\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "d7c80a60-0e9b-4d52-9cb7-28d6d9a2e6cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_above_5 = (y_train >'5')\n",
+    "y_train_even = (y_train.astype(int)% 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "74fdfb8c-3386-48b7-b35b-6e6eb4bb6a12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " y_train_ml = np.c_[y_train_above_5,y_train_even]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "3be82b5c-745a-4bca-b71e-2f445fcba5f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier()\n",
+    "knn.fit(X_train,y_train_ml)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "1eddc121-a4cb-452f-9151-0024bb6fd934",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False]])"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.predict([X_train[0]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "12617416-fb0c-4d27-9193-f17b800d160f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = cross_val_predict(knn,X_train,y_train_ml,cv =3 ) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "759d8f2b-a46c-42cf-adac-b18e47c4c37f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9781811708153896"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_train_ml,y_pred,average =\"macro\")\n",
+    "# weighted "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "0444b4cc-e048-4511-aca3-9b8db4b56d6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.multioutput import ClassifierChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "ac76c32e-87ec-440d-ba42-f28cd7ad2b7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain_svc = ClassifierChain(SVC(),cv=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "04cec366-7ee2-49e4-93a8-683df01beaca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ClassifierChain(cv=3, estimator=SVC())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ClassifierChain</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.multioutput.ClassifierChain.html\">?<span>Documentation for ClassifierChain</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">estimator&nbsp;</td>\n",
+       "            <td class=\"value\">SVC()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('order',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">order&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cv&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('chain_method',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">chain_method&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;predict&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('base_estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">base_estimator&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: SVC</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kernel',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kernel&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('degree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">degree&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">gamma&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('coef0',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">coef0&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shrinking',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">shrinking&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('probability',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">probability&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cache_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cache_size&nbsp;</td>\n",
+       "            <td class=\"value\">200</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">-1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('decision_function_shape',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">decision_function_shape&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('break_ties',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">break_ties&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "ClassifierChain(cv=3, estimator=SVC())"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain_svc.fit(X_train[:2000],y_train_ml[:2000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "b289425a-8883-4e18-a917-ef61d78e9de0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0.]])"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain_svc.predict([X_train[0]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65fc5051-121f-4b77-beb6-bc7dca7c82cc",
+   "metadata": {},
+   "source": [
+    "# multioutput"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "f8f104dd-f385-4c2d-91a2-2bdc54fbdfa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(40)\n",
+    "image_noise = np.random.randint(0,100,X_train.shape)\n",
+    "X_train_noisy = X_train + image_noise\n",
+    "image_noise = np.random.randint(0,100,X_test.shape)\n",
+    "X_test_noisy = X_test + image_noise\n",
+    "y_train_mc = X_train\n",
+    "Y_test_mc = X_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "f5cc97b2-2e85-4aad-8c44-1e70ff4173db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x200868f8a50>"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(X_train[0].reshape(28,28),cmap=\"binary\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "5618c259-2d69-4dbb-b74f-13eab655b9a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x200874b5bd0>"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(X_train_noisy[0].reshape(28,28),cmap=\"binary\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "cc86954b-70e9-4301-ba2e-e4de86263480",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-8 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier()\n",
+    "knn.fit(X_train_noisy,y_train_mc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "ff559246-6343-4854-838d-4ce365d554ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " pred = knn.predict([X_test_noisy[0]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "cc1be62d-68c9-4163-b3a2-50f3701acc68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x200875079d0>"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(pred.reshape(28,28),cmap=\"binary\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78113c69-e3b1-4627-b620-697b3346c639",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/projects/notebooks/classification-project/classification_notebook_polished.ipynb b/projects/notebooks/classification-project/classification_notebook_polished.ipynb
new file mode 100644
index 0000000..505aba1
--- /dev/null
+++ b/projects/notebooks/classification-project/classification_notebook_polished.ipynb
@@ -0,0 +1,3076 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "add886cf",
+   "metadata": {},
+   "source": [
+    "# 🔢 MNIST Classification — Complete Workflow\n",
+    "### Binary, Multiclass, Multilabel & Multioutput Classification\n",
+    "\n",
+    "This notebook covers the full classification arc on the MNIST handwritten\n",
+    "digits dataset, mirroring everything you built across the season:\n",
+    "\n",
+    "| Stage | What's covered |\n",
+    "|-------|-----------------|\n",
+    "| Binary Classification | Detect digit \"8\" with SGD, Random Forest, and a dummy baseline |\n",
+    "| Cross-Validation | `cross_val_score`, `cross_val_predict`, `StratifiedKFold` |\n",
+    "| Metrics | Confusion matrix, precision, recall, F1, decision function, PR curve, ROC curve, AUC |\n",
+    "| Multiclass | One-vs-Rest / One-vs-One with SVC |\n",
+    "| Multilabel | KNN predicting two independent yes/no labels at once |\n",
+    "| Multioutput | KNN denoising images (predicting a full image, not a label) |\n",
+    "\n",
+    "> **Note on metrics:** This notebook explains precision, recall, F1, k-fold CV,\n",
+    "> and decision functions in more depth than the rest, since these are the\n",
+    "> concepts most people get stuck on first.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05cf3896",
+   "metadata": {},
+   "source": [
+    "## 1. Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "49db1652",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.linear_model import SGDClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.dummy import DummyClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.multiclass import OneVsRestClassifier\n",
+    "from sklearn.multioutput import ClassifierChain\n",
+    "\n",
+    "from sklearn.model_selection import (\n",
+    "    cross_val_score, cross_val_predict, StratifiedKFold\n",
+    ")\n",
+    "from sklearn.base import clone\n",
+    "from sklearn.metrics import (\n",
+    "    confusion_matrix, ConfusionMatrixDisplay,\n",
+    "    precision_score, recall_score, f1_score,\n",
+    "    precision_recall_curve, roc_curve, roc_auc_score\n",
+    ")\n",
+    "\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "370d2dfe",
+   "metadata": {},
+   "source": [
+    "## 2. Loading MNIST\n",
+    "\n",
+    "MNIST is 70,000 images of handwritten digits (0-9), each flattened into a\n",
+    "784-length vector (28×28 pixels). We split it into the first 60,000 rows for\n",
+    "training and the last 10,000 for testing — this is the dataset's standard split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f2f46d13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape: (70000, 784), y shape: (70000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "mnist = fetch_openml(\"mnist_784\", as_frame=False)\n",
+    "X, y = mnist.data, mnist.target\n",
+    "print(f\"X shape: {X.shape}, y shape: {y.shape}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a955daf4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGZCAYAAABmNy2oAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQAADb5JREFUeJzt3VuIlfX+x/HveNbJ7RHyVGljGRqVUVkaHcEi1CAiy4vIRIxSL1JKsDKNKLIrrUgKi/CQIloKlmgHKIpQSCpBKclUYowUFQpzlOd/s/eX7V/d+VulM+nrBXOzWp/1PMu09zyr6amuqqoqACAiWjX3CQDQcogCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCLcLbb78ddXV1sWnTpr/l9erq6mLy5Ml/y2v992s+++yzNW137NgRdXV1J/x69913/9bzhL+iTXOfAJxLpkyZEuPGjTvmsUsuuaSZzgaOJwpwBl144YVx/fXXN/dpwEn5+Ih/jEOHDsW0adPiqquuii5dukT37t3jhhtuiPfff/+kmwULFsSll14a7du3j8GDB5/wo5rGxsaYNGlS9OvXL9q1axcDBgyI2bNnx5EjR07n24EWSRT4x/jjjz9i3759MX369Hjvvfdi6dKlceONN8Y999wT77zzznHPX716dcybNy/mzJkTK1asiIsuuigeeOCBWLFiRT6nsbExrrvuuli3bl0888wz8cEHH8SECRPihRdeiIkTJ/7pOfXv3z/69+9/yu/hxRdfjHbt2kWnTp3ixhtvjNWrV5/yFs6IClqAt956q4qIauPGjae8OXLkSNXU1FRNmDChGjp06DF/LSKqjh07Vo2Njcc8/7LLLqsGDhyYj02aNKk677zzqp9++umY/csvv1xFRLVly5ZjXnPWrFnHPK+hoaFqaGj403P9+eefq4kTJ1bLly+vPvvss2rx4sXV9ddfX0VE9cYbb5zye4bTTRRoEU41CsuXL6+GDx9e1dfXVxGRXx06dDjmeRFRjRo16rj9rFmzqoiodu3aVVVVVfXt27caPXp01dTUdMzXli1bqoioXnvttWNe8/9H4a84fPhwNXTo0KpHjx5VU1PT3/a68Ff4+Ih/jJUrV8Z9990Xffv2jUWLFsWXX34ZGzdujIcffjgOHTp03PN79ep10sf27t0bERF79uyJNWvWRNu2bY/5GjJkSERE/Prrr6ft/bRt2zbGjh0be/fuje+///60HQdK+Okj/jEWLVoUAwYMiGXLlkVdXV0+/scff5zw+Y2NjSd9rEePHhER0bNnz7jiiivi+eefP+Fr9OnT56+e9v9U/ft/fNiqle/PaBlEgX+Murq6aNeu3TFBaGxsPOlPH3300UexZ8+eOP/88yMi4ujRo7Fs2bJoaGiIfv36RUTEqFGjYu3atdHQ0BDdunU7/W/ivzQ1NcWyZcuiZ8+eMXDgwDN6bDgZUaBF+fjjj2PHjh3HPX7XXXfFqFGjYuXKlfHoo4/GvffeG7t27YrnnnsuevfufcKPX3r27Bm33XZbPP3001FfXx+vvfZabN269ZgfS50zZ06sX78+hg8fHlOnTo1BgwbFoUOHYseOHbF27dp4/fXXMyAn8p9/mP/www//8309/vjj0dTUFCNGjIhevXrFrl27Yv78+bF58+Z46623onXr1qf4KwSnlyjQojz55JMnfPzHH3+M8ePHxy+//BKvv/56LFy4MC6++OKYMWNG7N69O2bPnn3cZsyYMTFkyJB46qmnYufOndHQ0BCLFy+OsWPH5nN69+4dmzZtiueeey7mzp0bu3fvjs6dO8eAAQPizjvv/NOrh1P9bxkuv/zyWLBgQSxZsiQOHjwYnTt3zh+FHTly5Cm9BpwJddV/PtQE4Jzn324BkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAUpvmPgH4M0ePHi3eHDhw4DScyd/jlVdeqWn3+++/F2+2bdtWvHn11VeLN9OnTy/eLF26tHgTEdGhQ4fizYwZM4o3s2bNKt6cDVwpAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAguSHeWWbnzp3Fm8OHDxdvvvjii+LN559/XryJiNi/f3/xZsWKFTUd62xzwQUXFG+mTJlSvFm1alXxpnPnzsWbiIgrr7yyeHPzzTfXdKxzkSsFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgCkuqqqquY+CY739ddf17S77bbbijcHDhyo6VicWa1bty7eLFy4sHhTX19fvKlFnz59atp169ateDNo0KCajnUucqUAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkd0ltofbt21fTbtiwYcWb7du313Sss00tv3a13LHzk08+Kd5ERLRr16544w64lHKlAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGA1Ka5T4AT6969e027uXPnFm/WrFlTvBk6dGjxZurUqcWbWl111VXFmw0bNhRv6uvrizffffdd8SYiYt68eTXtoIQrBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoApLqqqqrmPgma18GDB4s3nTt3Lt5MmjSpeBMR8eabbxZvFi1aVLwZN25c8QbONq4UAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQ2jT3CdD8/vWvf52R43Tp0uWMHCeitpvo3X///cWbVq18X8XZxe9oAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAg1VVVVTX3SXBu+O2332rajR49unjz6aefFm8+/PDD4s3IkSOLN9CSuVIAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEByQzxavO3btxdvrr766uJN165dize33npr8eaaa64p3kREPPbYY8Wburq6mo7FucuVAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkhvicVZatWpV8Wb8+PHFm4MHDxZvavXCCy8Ubx588MHiTe/evYs3nD1cKQCQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAILkhHvzbt99+W7yZNm1a8WbDhg3Fm1o98sgjxZuZM2cWb/r27Vu8oWVypQBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgOSGePAX7N+/v3izZs2amo710EMPFW9q+eN9++23F2/Wr19fvKFlcqUAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkd0mFf4j27dsXb5qamoo3bdu2Ld6sW7eueHPLLbcUbzj9XCkAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACC1ae4TgJbim2++Kd6sWLGieLNx48biTURtN7erxeDBg4s3N91002k4E5qDKwUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACQ3xKPF27ZtW/Fm/vz5xZuVK1cWbxobG4s3Z1KbNuV/xHv37l28adXK95dnC38nAUiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQ3BCPmtRyI7glS5bUdKxXXnmleLNjx46ajtWSXXvttcWbmTNnFm/GjBlTvOHs4UoBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgDJDfHOMnv27CnebNmypXgzefLk4s3WrVuLNy3dsGHDijdPPPFETce6++67izetWvm+jzJ+xwCQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAMldUs+Affv2FW8mTZpU07E2b95cvNm+fXtNx2rJRowYUbyZNm1a8eaOO+4o3nTs2LF4A2eKKwUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKAKRz+oZ4X331VfHmpZdeKt5s3LixeLN79+7iTUvXqVOnmnZTp04t3sycObN4U19fX7yBs40rBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoApHP6hnirVq06I5szafDgwcWb0aNHF29at25dvJk+fXrxJiKia9euNe2Acq4UAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQ6qqqqpr7JABoGVwpAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJD+D5AeyRRQIFucAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(X[0].reshape(28, 28), cmap=\"binary\")\n",
+    "plt.title(f\"Label: {y[0]}\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "50078e10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4075960",
+   "metadata": {},
+   "source": [
+    "## 3. Binary Classification — \"Is it an 8?\"\n",
+    "\n",
+    "We convert the 10-class problem into a yes/no problem: is this digit an 8 or not?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "64ada266",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train8 = (y_train == \"8\")\n",
+    "y_test8 = (y_test == \"8\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc39f86f",
+   "metadata": {},
+   "source": [
+    "### 3.1 SGD Classifier (baseline linear model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "64ab9cc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False,  True])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgd = SGDClassifier(random_state=42)\n",
+    "sgd.fit(X_train, y_train8)\n",
+    "sgd.predict(X_train[-3:])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4ffe8aae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9446166666666667"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgd.score(X_train, y_train8)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "415c5936",
+   "metadata": {},
+   "source": [
+    "### 3.2 Cross-validated accuracy\n",
+    "\n",
+    "A single `.score()` call only tells you accuracy on the data the model trained\n",
+    "on — there's no guarantee it generalises. `cross_val_score` solves this by\n",
+    "splitting the training data into 5 folds (`cv=5`), training on 4, testing on the\n",
+    "5th, and repeating 5 times so every fold gets a turn as the test set.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "01164858",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.90666667, 0.93833333, 0.91566667, 0.80708333, 0.8995    ])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cross_val_score(sgd, X_train, y_train8, cv=5, scoring=\"accuracy\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19d87f55",
+   "metadata": {},
+   "source": [
+    "### 3.3 Why accuracy alone is misleading — the Dummy baseline\n",
+    "\n",
+    "About 90% of digits are *not* an 8. A lazy classifier that always predicts\n",
+    "\"not 8\" would already be ~90% accurate while being completely useless.\n",
+    "`DummyClassifier` exists specifically to expose this trap.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "93adfcb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy_c = DummyClassifier()\n",
+    "dummy_c.fit(X_train, y_train8)\n",
+    "dummy_c.predict(X_train).sum()   # how many times it predicted \"True\" — should be 0\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "84393911",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.9025    , 0.9025    , 0.9025    , 0.9025    , 0.90241667])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cross_val_score(dummy_c, X_train, y_train8, cv=5, scoring=\"accuracy\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b240c3b2",
+   "metadata": {},
+   "source": [
+    "**Reading this number:** if the dummy baseline scores ~0.90 and your SGD\n",
+    "model also scores ~0.90, your model has learned *nothing* — it's no better\n",
+    "than always guessing \"not 8\". This is exactly why precision/recall/F1 (next\n",
+    "section) matter more than raw accuracy for imbalanced classes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17268c63",
+   "metadata": {},
+   "source": [
+    "### 3.4 What k-fold cross-validation is actually doing\n",
+    "\n",
+    "This manual loop is the same thing `cross_val_score` does internally —\n",
+    "written out so you can see every step:\n",
+    "\n",
+    "* `StratifiedKFold(n_splits=5)` splits the data into 5 folds, but **stratified**\n",
+    "  means each fold keeps the same ratio of 8s to non-8s as the full dataset.\n",
+    "  Without this, an unlucky fold could end up with very few 8s, making the score\n",
+    "  for that fold unreliable.\n",
+    "* For each of the 5 iterations: clone a fresh, untrained copy of the model\n",
+    "  (`clone(sgd)`), train it on 4 folds, test it on the 1 held-out fold.\n",
+    "* `correct_predictions / len(y_pred)` is just accuracy for that single fold.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "916635a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9066666666666666\n",
+      "0.9383333333333334\n",
+      "0.9156666666666666\n",
+      "0.8070833333333334\n",
+      "0.8995\n"
+     ]
+    }
+   ],
+   "source": [
+    "skf = StratifiedKFold(n_splits=5)\n",
+    "\n",
+    "for train_i, test_i in skf.split(X_train, y_train8):\n",
+    "    cloned_sgd = clone(sgd)\n",
+    "    X_train_f, y_train8_f = X_train[train_i], y_train8[train_i]\n",
+    "    X_test_f, y_test_f = X_train[test_i], y_train8[test_i]\n",
+    "\n",
+    "    cloned_sgd.fit(X_train_f, y_train8_f)\n",
+    "    y_pred = cloned_sgd.predict(X_test_f)\n",
+    "    correct_predictions = (y_pred == y_test_f).sum()\n",
+    "    print(correct_predictions / len(y_pred))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c424410",
+   "metadata": {},
+   "source": [
+    "### 3.5 Getting predictions for every training sample — `cross_val_predict`\n",
+    "\n",
+    "`cross_val_score` only gives you the **score** for each fold. `cross_val_predict`\n",
+    "gives you the actual **predictions** — each sample's prediction comes from the\n",
+    "fold where it was in the test set, so you still never get a prediction that\n",
+    "\"cheated\" by training on its own answer. This is essential for the metrics below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ddb1e9a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = cross_val_predict(sgd, X_train, y_train8, cv=5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37f91c30",
+   "metadata": {},
+   "source": [
+    "## 4. The Confusion Matrix — the foundation of every metric below\n",
+    "\n",
+    "A confusion matrix counts how predictions split against the truth:\n",
+    "\n",
+    "```\n",
+    "                 Predicted: Not 8     Predicted: 8\n",
+    "Actual: Not 8    True Negative (TN)   False Positive (FP)\n",
+    "Actual: 8        False Negative (FN)  True Positive (TP)\n",
+    "```\n",
+    "\n",
+    "* **TN** — correctly said \"not 8\"\n",
+    "* **TP** — correctly said \"8\"\n",
+    "* **FP** — said \"8\" but it wasn't (a false alarm)\n",
+    "* **FN** — said \"not 8\" but it actually was an 8 (a miss)\n",
+    "\n",
+    "Every metric below (precision, recall, F1) is just a different ratio of these\n",
+    "four numbers.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ecf97e36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[49148,  5001],\n",
+       "       [ 1392,  4459]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_train8, y_pred)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4b2ae52",
+   "metadata": {},
+   "source": [
+    "## 5. Precision, Recall, F1 — explained in depth\n",
+    "\n",
+    "These three numbers are the ones students get stuck on most, so here's what\n",
+    "each one is really asking:\n",
+    "\n",
+    "### Precision — \"Of everything I called an 8, how many actually were?\"\n",
+    "\n",
+    "$$\\text{Precision} = \\frac{TP}{TP + FP}$$\n",
+    "\n",
+    "If precision = 0.85, it means: **when the model says \"this is an 8,\" it's\n",
+    "right 85% of the time.** A model can cheat at precision by only ever guessing\n",
+    "\"8\" when it's extremely confident — it becomes very picky, missing a lot of\n",
+    "real 8s in the process, but rarely wrong when it does guess.\n",
+    "\n",
+    "### Recall — \"Of all the real 8s, how many did I catch?\"\n",
+    "\n",
+    "$$\\text{Recall} = \\frac{TP}{TP + FN}$$\n",
+    "\n",
+    "If recall = 0.75, it means: **out of every real 8 in the data, the model\n",
+    "correctly found 75% of them** — and missed the other 25%. A model can cheat\n",
+    "at recall by guessing \"8\" very liberally, catching almost every real 8, at the\n",
+    "cost of a lot of false alarms (which tanks precision).\n",
+    "\n",
+    "### The precision/recall tradeoff\n",
+    "\n",
+    "These two numbers pull in opposite directions. Being more cautious (raising\n",
+    "the threshold to say \"8\") increases precision but lowers recall. Being more\n",
+    "liberal does the reverse. There's no single \"right\" number — it depends on\n",
+    "the cost of a false positive vs. a false negative for your specific problem.\n",
+    "(E.g. a cancer-screening model should favor recall — missing a real case is\n",
+    "worse than a false alarm. A spam filter should favor precision — wrongly\n",
+    "blocking a real email is worse than missing one piece of spam.)\n",
+    "\n",
+    "### F1 score — a single number balancing both\n",
+    "\n",
+    "$$F1 = 2 \\times \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}$$\n",
+    "\n",
+    "F1 is the **harmonic mean** of precision and recall (not the regular average).\n",
+    "The harmonic mean punishes imbalance — if either precision or recall is very\n",
+    "low, F1 will be low too, even if the other one is high. This makes F1 useful\n",
+    "when you want one number that's only high if *both* precision and recall are\n",
+    "reasonably good.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3b8fafaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.47135306553911205"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(y_train8, y_pred)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e16611ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.762091950094001"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(y_train8, y_pred)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1efbb754",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5824570570178303"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_train8, y_pred)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a1ecc9b",
+   "metadata": {},
+   "source": [
+    "## 6. The Decision Function — what's actually happening under the hood\n",
+    "\n",
+    "Every classifier has to make a **yes/no** decision somehow. For linear models\n",
+    "like SGD, this happens via `decision_function()` — it computes a raw, signed\n",
+    "number for each sample:\n",
+    "\n",
+    "* **Positive number** → model leans toward \"yes, this is an 8\" (further from\n",
+    "  zero = more confident)\n",
+    "* **Negative number** → model leans toward \"no, not an 8\"\n",
+    "* **The default decision threshold is 0** — anything above 0 gets classified\n",
+    "  as \"8\", anything below as \"not 8\"\n",
+    "\n",
+    "This is the *real* mechanism behind `.predict()` — `.predict()` is just\n",
+    "`decision_function() > 0` under the hood.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b3dd1b8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-12954.44836362])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgd.decision_function([X_train[0]])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58827f8e",
+   "metadata": {},
+   "source": [
+    "**Why this matters:** because the threshold (0) is something *you* can move.\n",
+    "Raising the threshold (e.g. requiring score > 2 instead of > 0) makes the\n",
+    "model more cautious → higher precision, lower recall. Lowering it does the\n",
+    "opposite. The PR curve below visualizes exactly this tradeoff across every\n",
+    "possible threshold."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7d9e4bf2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_scores = cross_val_predict(sgd, X_train, y_train8, cv=5, method=\"decision_function\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "770a9be0",
+   "metadata": {},
+   "source": [
+    "## 7. Precision-Recall Curve\n",
+    "\n",
+    "`precision_recall_curve` computes precision and recall at **every possible\n",
+    "threshold value**, letting you see the full tradeoff instead of just one\n",
+    "fixed point.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c664513a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "thresholds shape: (60000,)\n",
+      "precision shape : (60001,)\n",
+      "recall shape    : (60001,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "prec, recall, thresholds = precision_recall_curve(y_train8, y_scores)\n",
+    "print(\"thresholds shape:\", thresholds.shape)\n",
+    "print(\"precision shape :\", prec.shape)\n",
+    "print(\"recall shape    :\", recall.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7087a3d",
+   "metadata": {},
+   "source": [
+    "Note `prec` and `recall` have one more entry than `thresholds` — sklearn adds\n",
+    "a final point representing threshold = +infinity (precision=1, recall=0), so\n",
+    "the curve has a clean end point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9696bf2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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urf5cuWVnZ4dx48bhyy+/xL///osPP/wQv/32G9q0aYOgoCAEBwejbNmyePz4MS5duoTTp09jzZo1AIBp06Zhx44daNq0KcaPH48aNWogPj4eO3fuxJgxY1CtWjW0a9cO69evx9ChQ9G1a1fcvXsXX3/9NVxcXPR+qq1///4YNGgQoqKi0LBhwywhz8/PD+3atUPNmjVhZ2eHS5cuYfny5fD399eEqb/++gv9+/fH4sWL0bdvX73Wl18eHh6YNm0aJkyYgJs3b2r6vj148AD//fef5sjbuXPnMHz4cHzwwQeoXLkyzMzMsG/fPpw7dw5fffVVnrc7efJkbN26FQEBAZg0aRLs7e3xzz//YNu2bZg5cyYUCkWOy3799ddo3bo1WrZsic8++wwZGRn4/vvvYWVlhcePH+uyO4jeTupe8USGoL5y6eTJk29s169fP2FlZZXte2lpaeLHH38UtWrVEubm5sLa2lpUq1ZNDBo0SFy7dk2r7V9//SXq1aunaVenTh2tq49ev8pv69atok2bNqJs2bLCzMxMODo6irZt24rDhw9r2uR0tdnhw4dF8+bNhZWVlbCwsBANGjQQW7ZsydXnV18Ztn///jfuFyGEOHjwoGjTpo0oVaqUMDU1FRUqVBCffvqpiIyMFEeOHBHm5uaiSZMm4tmzZzmu403fQ3JysihfvryoXLmySE9PF0IIcfbsWdGtWzfh6OgoTE1NhbOzs2jevLlYuHCh1rJ3794V/fv3F87OzsLU1FS4urqKbt26iQcPHmjafPfdd8LDw0PI5XLh5eUlfv/9dzF58uQsV3vl9yo/tYSEBGFhYSEAiN9//z3L+1999ZXw9fUVdnZ2Qi6XiwoVKojRo0eLuLi4LPspt9sUIndX+b333ntZ5jdr1kw0a9Ysy7Zz+l3ZuHGjCAgIELa2tkIulwt3d3fRtWtXsWfPHiGEEA8ePBDBwcGiWrVqwsrKSlhbW4uaNWuKn376SfO95qUeIYSIiIgQ7du3FwqFQpiZmYlatWpl2Tc5fU+bN28WNWvWFGZmZqJ8+fLiu+++y/Z7J9I3mRBCSJLkiIiIiIoJXuVHREREpCMGKiIiIiIdMVARERER6YiBioiIiEhHDFREREREOmKgIiIiItJRkRjYU6lUIioqCjY2NnoZuJCIiIgoJ0IIPH36FK6urjAyyt2xpyIRqKKiovJ8nzQiIiIiXdy9e/etN2NXKxKBSn0j2rt372rdsJWIiIhI3xITE+Hm5qbJH7lRJAKV+jSfra0tAxUREREViLx0M2KndCIiIiIdMVARERER6YiBioiIiEhHDFREREREOmKgIiIiItIRAxURERGRjhioiIiIiHTEQEVERESkIwYqIiIiIh0xUBERERHpKM+B6tChQ2jfvj1cXV0hk8mwcePGty5z8OBB+Pj4wNzcHBUqVMDChQvzUysRERFRoZTnQPX8+XPUqlULv/zyS67a37p1C23btkWTJk0QHh6O8ePHY8SIEVi3bl2eiyUiIiIqjPJ8c+Q2bdqgTZs2uW6/cOFClC9fHnPmzAEAeHl54dSpU/jxxx/RpUuXvG6eiIiIqNDJc6DKq9DQULRq1UprXlBQEP7880+kpaXB1NQ0yzIpKSlISUnRvE5MTDRojT/99BOWLFmS5+Xychfq/C5TENsoqGUKa10FtUxhrSs/y1haWsLMzAwymeyND/W6X3+YmJjA2toaJiYmMDIy0jyMjY1hZGQEMzMzWFtba16/+jAzM4ONjU227xkZGcHS0hJWVlYwMTGBiYkJ5HI5FAqF5rX6PaJi6fZtIDgYePxY6koMr0EDYNEiqavQMPi/KjExMXByctKa5+TkhPT0dMTFxcHFxSXLMjNmzMDUqVMNXZpGTEwMIiIiCmx7RCStMmXKwMTEBMbGxjA2NoaVlRUUCgVsbGzg5uYGV1dXlC5dGmZmZjA3N4e3tzfq1KnDIEaF34YNwMGDUldRMF7LFlIrkH8dXv/frxAi2/lq48aNw5gxYzSvExMT4ebmZrD6Bg4cmOUo2tuoP4MhlymIbRTUMoW1roJaprDWlZ9lMjIy8OzZMyiVSggh8vV4/vw5UlNToVQqszwyMjKQmJiI9PR0zetX33/69GmOy6ampmqWVT8SEhK0jngDwMOHD/O8nxQKBerVqwdra2u4ubnBy8sLnTt3zvIfRqICdfo0cOcOULs24OEBPH2qmt+uHTBypJSVGZ69vdQVaDF4oHJ2dkZMTIzWvNjYWJiYmKB06dLZLiOXyyGXyw1dmkalSpVQqVKlAtseERUsIQSUSiXS0tJw9+5dvHjxAhkZGcjIyEBqairi4+Px4sULxMbG4t69e4iOjsazZ8+QlpaG6OhonDx5EgkJCdizZ4/WeocOHQofHx+0aNECNWrUQOnSpeHk5ARXV1c4Ojrm6/QrUa6lpgLNmgHPnqle29tnnuqrWhVo0UK62koggwcqf39/bNmyRWve7t274evrm23/KSIifZPJZJrTe5UrV87z8unp6di3bx/u37+P58+fIywsDJs2bcKTJ08QFhaGsLCwLMu4ubmhZcuW6NevH5o2baqPj0EEXLkCTJ0KlCoFuLtnhilAu9+Us3OBl1bSyUQej/c/e/YM169fBwDUqVMHs2fPRkBAAOzt7VG+fHmMGzcO9+/fx19//QVANWyCt7c3Bg0ahIEDByI0NBSDBw/GihUrcn2VX2JiIhQKBRISEmBra5vHj0hEZBg3btzAL7/8gqioKMTExODhw4eIjY3Fo0ePtNqpTxG+//77+Pjjj9kXi/Lv88+BWbOyzn/6FDh/Hjh1CkhPBz76CFAoCr6+YiI/uSPPgerAgQMICAjIMr9fv35YunQpgoODcfv2bRw4cEDz3sGDBzF69GhcuHABrq6uGDt2LAYPHpzrbTJQEVFRkpiYiM2bN2P16tVZjtA7OjqiVq1aCAoKwqeffgozMzOJqqQi5/ffgU8+yXzdurUqQHl5qTqi8xSz3hRIoJICAxURFVWxsbE4e/YsDh48iF9//RXx8fGa98qWLYvAwED4+fnBy8sLjRo1YsCinLVuDezapZpevFh1FIoMgoGKiKgQe/bsGbZs2YKtW7di69atWcbYs7Ozw4gRIzBs2DCUKVNGoipJUtHRwM8/q446de4MWFmpOp8/ewaoL+Rq0QLYvh1gP2SDYaAiIioikpKSsGXLFpw4cUJzBCsjI0PzfkBAAAYOHIguXbrwqFVJYmOT2dHc2hro2RO4ehU4dAhQ/7n+5x+gVy/paiwBGKiIiIqopKQkzJs3D4sWLcKtW7c08+3t7dGnTx9MmjQJ9oVs3B3SI6USGDAAWLr0ze1kMiA2FnBwKJCySqr85I483xyZiIj0z9LSEl999RVu3LiB8+fPIzg4GADw+PFjzJ07F15eXjh27Ji0RZJ+xMWphjhQKoEdO4A+fQBjY+0wdfkysGYNUL585rw+fYA//mCYKqR4hIqIqJBKTk7Gb7/9hunTp2uGYvjkk08wfPhw1KhRQ+LqKF+SklT9oszMVH2jsvPhh8Dy5arp+HhV0DI2Bj79tKCqLPF4yo+IqBiKjIzEsGHDsHXrVgCAkZERfv311zwNP0MSESJzOIPY2Lfff278eGD6dA6BIDGe8iMiKobKly+PLVu2YPny5ahduzaUSiWGDBmCcePGIS0tTerySC0xUXVvPSFUHct79gSMjFSn7bp3zzlMTZgAXLqkOgX4zTcMU0UUj1ARERUh6enpaN++PXbu3AkAqFKlCpYuXQp/f3+JKyvBLl9WDbh5+HDulzlyRHXrmEePgFq1DFcb5QuPUBERFXMmJibYsmULvv32W1hYWODq1ato2LAhNm/eLHVpJVNysmrMqNyGqSpVgLQ0oFEjoFw5hqlihIGKiKiIMTExwbhx43DlyhV4e3sDADp27Iiff/5Z4spKoKNHc34vOFh1Xz0hgAsXVH2orlwBeC/HYomn/IiIirC4uDgMHDgQGzduhJGRETZu3Ij27dtLXVbJ8Wp/p4wMVZ+pFy9UV/Nx3LAii6f8iIhKGAcHB6xbtw4dO3aEUqnE+++/zyNVBaVlS+3XRi//pJqbM0yVQAxURERFnJGREVasWAFfX19kZGRgxIgRGD9+PIrACYii5fFj1RGpRo1UY0hFR2e+FxYmXV1UKDBQEREVAxYWFjhx4gS6d+8OAJgxYwa+/PJLiasqRnbsyLw58bFjgFyu6hcFALt3A3XrSlcbFQoMVERExYSRkRGWL1+OgIAAAMCPP/6IBQsWSFxVEScEcO0a0LZtzm1eXhhAJRsDFRFRMWJqaoq9e/fik08+AQAMHz4ce/fulbiqImjKFNXpPSMj1VAHr1qyJHO6fn3AxaVAS6PCiVf5EREVQ0IIvPfee9ixYwccHR1x+vRplC1bVuqyigYhMjuYv06p5EjmJQCv8iMiIgCATCbDn3/+CXNzc8TGxqJr167IyMiQuqyiITAw67xvvgFSUhimKEcMVERExZSLiwuOHDkCADh+/DgmT54scUVFwPHjwP79ma+FUD3GjwfMzKSriwo9BioiomLMx8cHs2bNAgB88803CAkJkbiiQu7VeyJ26yZdHVTkMFARERVzY8aMQZcuXQAAffv2RXJyssQVFVLffZc5XakSsGqVdLVQkcNARURUAixevBgKhQIxMTH46aefpC6ncBo3LnP62jXp6qAiiYGKiKgEsLW1xddffw0AmDJlCk6fPi1xRYXMq53NR42SrAwquhioiIhKiKFDh6Jx48ZIS0vDiBEjpC6n8Ni5U/v17NnS1EFFGgMVEVEJYWxsjN9//x0ymQxHjx7Ftm3bpC6pcGjTJnP62TMOjUD5wkBFRFSCVKtWDf379wcAfPjhh4iJiZG4IoklJmq/trKSpg4q8hioiIhKmB9++AEVK1ZEfHw8RpX0/kIKReb0f/9JVwcVeQxUREQljJ2dHRYvXgwAWLVqlWbwzxLn5k3t1/XqSVMHFQsMVEREJVDTpk3RunVrAEDLli1x7tw5iSuSwKuDeD55Il0dVCwwUBERlVDLli1D1apV8eLFCwwcOBDp6elSl1Rw0tOB2FjVtLMzUKqUpOVQ0cdARURUQjk6OmLnzp2wsrLCf//9h0WLFkldUsG4ehUwNc18vWuXdLVQscFARURUgnl4eGDSpEkAgIkTJyIlJUXiigrAypXar2vWlKYOKlYYqIiISrhPP/0Utra2ePz4Mb7//nupyzG8yZMzp18dg4pIBwxUREQlnIWFBT799FMAwOzZs5GUlCRxRQZ0507m9NdfA9u3S1cLFSsMVEREhClTpqBMmTJISEjA1KlTpS7HcDw8MqdHj5asDCp+GKiIiAgmJiaamyf/8MMPuHz5ssQVGcDPP2u/5qjopEcMVEREBAAYNGgQ2rRpAyEEpk2bJnU5+qVUAq/eEDojQ7paqFhioCIiIo3JLztsr1ixAnde7W9U1K1fnznt7g4Y8c8f6Rd/ooiISMPPzw9+fn4AgF9//VXiavTogw8yp2/flqwMKr4YqIiISMtnn30GAFi8eDFSU1MlrkYPZLLM6enTpauDijUGKiIi0vL+++/D1dUVjx49woYNG6QuRzevnuoDgFGjJCmDij8GKiIi0mJiYoK+ffsCAJYuXSptMbrq0iVz+uFDXtlHBsNARUREWXz00UcAgJ07d+LChQsSV6MHjo6Ag4PUVVAxxkBFRERZVKlSBe+99x4AYMiQIRJXk0/h4ZnTx45JVweVCAxURESUrekvO3AfPnwYZ86ckbaYvEpKAurWzXxdsaJ0tVCJwEBFRETZql27Nrq87INU5G5HM3Zs5rStrXR1UIkhE0IIqYt4m8TERCgUCiQkJMCWvxhERAUmPDwcdV8e6Xn48CEcikI/pIYNgdBQ1bSJCZCWJm09VOTkJ3fwCBUREeWoTp06qFatGgBg7dq1EleTC99+mxmmAOD0aelqoRKFgYqIiN7og5ejjC9atAiF+qRGXBwwYULm64kTgRo1pKuHShSe8iMiojeKjo6Gq6srAODkyZPw9fWVuKIcvDoiulwOvHghXS1UpPGUHxER6Z2Liwvef/99AMDy5cslriaXGKaogDFQERHRW3Xv3h0AcOTIEYkryUF6eub0N99IVweVWAxURET0Vg0aNAAAnDlzBvfu3ZO4mmwcPJg5/eqQCUQFhIGKiIjeyt3dHb6+vlAqldi/f7/U5WTVokXmtLGxdHVQicVARUREudK4cWMAqvv7FSqF/9oqKgEYqIiIKFeCgoIAANu2bUNaYRoss3PnzOnz56Wrg0o0BioiIsqVFi1aoHTp0khISMDevXulLifTxo2Z0++8I1kZVLIxUBERUa6YmJigQ4cOAIDNmzdLXM1LCQmZ0x99JF0dVOIxUBERUa6pR01fu3YtUlJSJK4GwL//Zk4vXixdHVTi5StQzZ8/H56enjA3N4ePjw8OHz78xvb//PMPatWqBUtLS7i4uOCjjz7Co0eP8lUwERFJp2XLlnBycsLDhw+xbt06qcsBVq5UPZcuLW0dVOLlOVCtWrUKo0aNwoQJExAeHo4mTZqgTZs2iIyMzLb9kSNH0LdvXwwYMAAXLlzAmjVrcPLkSXz88cc6F09ERAXLxMQELV4OUbB+/Xppi7lzBzh0SDU9Zoy0tVCJl+dANXv2bAwYMAAff/wxvLy8MGfOHLi5uWHBggXZtj9+/Dg8PDwwYsQIeHp6onHjxhg0aBBOnTqlc/FERFTwhgwZAgDYsWMHkpKSpCtk4sTM6fbtpauDCHkMVKmpqQgLC0OrVq205rdq1QrHjh3LdpmGDRvi3r172L59O4QQePDgAdauXYv33nsvx+2kpKQgMTFR60FERIWDv78/3N3dkZSUhLVr10pXyKv3FaxRQ7o6iJDHQBUXF4eMjAw4OTlpzXdyckJMTEy2yzRs2BD//PMPunfvDjMzMzg7O6NUqVL4+eefc9zOjBkzoFAoNA83N7e8lElERAZkZGSEXr16AQBmzpyJjIyMgi/i6NHM6aVLC377RK/JV6d0mUym9VoIkWWe2sWLFzFixAhMmjQJYWFh2LlzJ27duoXBgwfnuP5x48YhISFB87h7925+yiQiIgMZOXIkbG1tceHCBezZs6fgC3g5ajsAoF+/gt8+0WvyFKgcHBxgbGyc5WhUbGxslqNWajNmzECjRo3wxRdfoGbNmggKCsL8+fOxePFiREdHZ7uMXC6Hra2t1oOIiAoPJycndO3aFQAKPlAVhuEaiF6Tp0BlZmYGHx8fhISEaM0PCQlBw4YNs10mKSkJRkbamzF+eeNKwfsvEREVWYGBgQCAdevWQalUFtyGzc0zp1+8KLjtEr1Bnk/5jRkzBn/88QcWL16MS5cuYfTo0YiMjNScwhs3bhz69u2rad++fXusX78eCxYswM2bN3H06FGMGDEC9evXh6urq/4+CRERFagOHTrA3Nwct27dwunTp6UpQi6XZrtErzHJ6wLdu3fHo0ePMG3aNERHR8Pb2xvbt2+Hu7s7ACA6OlprTKrg4GA8ffoUv/zyCz777DOUKlUKzZs3x/fff6+/T0FERAXO2toarVq1wubNm7FixQr4+voafqOvHpG6cMHw2yPKJZkoAufdEhMToVAokJCQwP5URESFyPz58zFs2DDUrVsXYWFhht/gV18B6v+QK5VADhdEEekiP7mDgYqIiPItOjpa033j9u3bmrMVBvNqgCr8f76oiMpP7uDNkYmIKN9cXFzg5+cHAFixYoVhN8ZBnqkQY6AiIiKddO/eHQCwbds2w25IocicPnPGsNsiyiMGKiIi0kmnTp0AAEePHsWDBw8Ms5HXh2WoVcsw2yHKJwYqIiLSiaenJ+rUqQMhBHbv3m2YjZw8mTnNgT2pEGKgIiIinQUEBABAloGf9aZBg8xpMzPDbINIBwxURESks7Zt2wIA9u3bx7tgUInEQEVERDpr0KAB5HI57t+/j4sXLxpuQ19/bbh1E+mAgYqIiHRmZWWlOe23c+dO/a781bGnRo7U77qJ9ISBioiI9KJVq1YAgH///ddwp/1sbAyzXiIdMVAREZFefPjhhzA2Nsbp06cRHh6u/w3wHrBUiDFQERGRXpQpUwbt27cHAPzxxx/6WWl0dOZ0hw76WSeRATBQERGR3gQHBwNQjZqul9N+mzdnTletqvv6iAyEgYqIiPSmefPmMDIyQmRkJCIjI3Vf4fjxquegIO3O6USFDAMVERHpjY2NjeZmyevWrdNtZUIAjx+rpqtX17EyIsNioCIiIr3q1asXAGDzq6fr8uPUqczpceN0WxeRgTFQERGRXgUGBgIAQkND8eTJk/yvaN++zOkyZXSsisiwGKiIiEivqlWrhqpVqyI1NRVbt27N/4q++kr1/PHH+imMyIAYqIiISK9kMhlat24NADh06JDuK2zaVPd1EBkYAxUREemd+mbJ+R4+4dXb17RooaeqiAyHgYqIiPSuWbNmsLKyQnR0dP5GTR80KHPaxUV/hREZCAMVERHpnVwuR8uWLQEAGzZsyPsK9DGGFVEBYqAiIiKD6NGjBwBgwYIFSE5Ozv2C8fGZ00uX6rUmIkNhoCIiIoN4//334erqikePHmHv3r25X3DLlszpvn31XxiRATBQERGRQZiZmaFNmzYAgMOHD+d+wW+/VT23a8fbzVCRwUBFREQG0/TlkAd5ClSXL6ue7e0NUBGRYTBQERGRwagD1X///YeEhIS3L3DnTub0l18aqCoi/WOgIiIig/Hw8EDFihWRkZGBna+OLZXzApnT77xjsLqI9I2BioiIDKpVq1YAgJMnT0pcCZHhMFAREZFB1a1bF0AuAlVsbOa0QmHAioj0j4GKiIgMqn79+gCA06dPQ6lU5txw3LjM6SdPDFwVkX4xUBERkUG98847MDc3x7Nnz3D+/PmcGy5enDnN4RKoiGGgIiIigzI2NkaDBg0AALt27cq+0YsXmdOdOxdAVUT6xUBFREQG16lTJwBvCFQDBmROr1hh+IKI9IyBioiIDK5169YAVAN8Pn/+PGuD27czp83MCqYoIj1ioCIiIoOrUqUKypcvj9TUVBw6dEj7TSGAY8dU0zt2FHxxRHrAQEVERAYnk8kQFBQEIJvTfrduZU43blyAVRHpDwMVEREVCPUAn1kC1avjU1lbF2BFRPrDQEVERAUiMDAQRkZGuHz5MiIjIzPfOHFC9TxsmDSFEekBAxURERUIOzs7+Pn5AQB2796d+YY6UL18j6goYqAiIqICk+W0X1oacPq0apqBioowBioiIiow6o7pe/bsQUZGhurqPvWgnpUrS1gZkW4YqIiIqMDUq1cPCoUC8fHxOHv2rHa/Kd5uhoowBioiIiowJiYm8PX1BQDVeFQXLqjesLGRsCoi3TFQERFRgQoMDAQA7Ny5M3PmvHkSVUOkHwxURERUoNT9qE4ePw6hntmsmWT1EOkDAxURERWo6tWrw8jICI8TEhCtnunpKWVJRDpjoCIiogJlbm6OKlWqAAAiJK6FSF8YqIiIqMDVqFEDwMtA9ddfktZCpA8MVEREVOBqVKoEADgLAC8H+yQqyhioiIiowNUQqu7oYWZmgJOTxNUQ6Y6BioiICly9uDgAwJW0NMTHx0tbDJEeMFAREVGBKxsWhqoAlEJgx44dUpdDpDMGKiIiKlhPnwLh4Wjx8uXBgwclLYdIHxioiIioYJ08CQCaQHX48GHpaiHSEwYqIiIqWGfPAgDqe3kBAC5fvoyUlBQpKyLSGQMVEREVrNmzAQAuPXtCoVBAqVTi6tWrEhdFpBsGKiIiKlj37gEAZObmqFatGgDg4sWLUlZEpDMGKiIiKjgZGZnTgYGoXbs2AODEiRPS1EOkJ/kKVPPnz4enpyfMzc3h4+Pz1g6FKSkpmDBhAtzd3SGXy1GxYkUsXrw4XwUTEVERdu5c5nStWnj33XcBAFu3bpWmHiI9McnrAqtWrcKoUaMwf/58NGrUCL/99hvatGmDixcvonz58tku061bNzx48AB//vknKlWqhNjYWKSnp+tcPBERFTGhoZnTxsYICgqCTCbDtWvXEBUVBVdXV+lqI9KBTIiX4//nkp+fH+rWrYsFCxZo5nl5eaFTp06YMWNGlvY7d+5Ejx49cPPmTdjb2+eryMTERCgUCiQkJMDW1jZf6yAiokKgRQtg716gUydgwwYAQN26dREeHo6VK1eie/fu0tZHhPzljjyd8ktNTUVYWBhavXYjy1atWuHYsWPZLrN582b4+vpi5syZKFu2LKpUqYLPP/8cycnJOW4nJSUFiYmJWg8iIioG9u5VPVtZaWY1bdoUAAf4pKItT4EqLi4OGRkZcHrtRpZOTk6IiYnJdpmbN2/iyJEjOH/+PDZs2IA5c+Zg7dq1GDZsWI7bmTFjBhQKhebh5uaWlzKJiKgwun07c3riRM1kkyZNAADHjx8v4IKI9CdfndJlMpnWayFElnlqSqUSMpkM//zzD+rXr4+2bdti9uzZWLp0aY5HqcaNG4eEhATN4+7du/kpk4iICpNXL2CqWlUzqR464ebNm8hjLxSiQiNPgcrBwQHGxsZZjkbFxsZmOWql5uLigrJly0KhUGjmeXl5QQiBey/HInmdXC6Hra2t1oOIiIq4+/dVz507a82uWLEijI2NkZCQkOPfBaLCLk+ByszMDD4+PggJCdGaHxISgoYNG2a7TKNGjRAVFYVnz55p5l29ehVGRkYoV65cPkomIqIi6cIF1XOdOlqzzc3N8c477wAAwsPDC7oqIr3I8ym/MWPG4I8//sDixYtx6dIljB49GpGRkRg8eDAA1em6vn37atr36tULpUuXxkcffYSLFy/i0KFD+OKLL9C/f39YWFjo75MQEVHh9vIefqhVK8tbdV6GrNOnTxdkRUR6k+dA1b17d8yZMwfTpk1D7dq1cejQIWzfvh3u7u4AgOjoaERGRmraW1tbIyQkBPHx8fD19UXv3r3Rvn17zJs3T3+fgoiICrf0dCAiQjVdo0aWt+vWrQuAgYqKrjyPQyUFjkNFRFTEnTwJ1K+vms7IAIy0/z9/+PBhNG3aFC4uLoiKipKgQKJMBh+HioiIKF/Wrs2cNsr6p0d9T7/o6Gg8efKkgIoi0h8GKiIiMrxly1TPDRpk+7aNjQ3Kli0LALh8+XJBVUWkNwxURERkeA8eqJ6Dg3Ns4uXlBQA49+oNlImKCAYqIiIyrIcPM6e7dMmxWY2XndVPnjxp6IqI9M5E6gKIiKiY+/vvzGkHhxybNW/eHD/99BN27979xjtwFEYZGRlIS0uTugzKAzMzMxhl058vvxioiIjIsPbtUz2/ZTDn5s2bw9jYGHfv3sX9+/eLxODPQgjExMQgPj5e6lIoj4yMjODp6QkzMzO9rI+BioiIDGvrVtXz1KlvbGZpaQlvb2+cPXsWJ0+eLBKBSh2mHB0dYWlpWaSOqpVkSqUSUVFRiI6ORvny5fXyvTFQERGR4SiVmdPVq7+1ua+vL86ePYtTp07h/fffN2BhusvIyNCEqdKlS0tdDuVRmTJlEBUVhfT0dJiamuq8PnZKJyIiw7lzJ3Pa1/etzevVqwcAOHXqlKEq0ht1nylLS0uJK6H8UJ/qy8jI0Mv6GKiIiMhwTpxQPdetC5i8/aSI78vQderUKRSBG3kAAE/zFVH6/t4YqIiIyHAOHVI9N22aq+Y1atSAmZkZHj9+jFu3bhmwMCL9YqAiIiLDyWOgMjMzQ61atQBwPKri5MCBA5DJZLm6GjIvbQsTBioiIjKMpCTg0iXVdA63nMnOq6f9qHho2LAhoqOjoVAo9Nq2MGGgIiIiw4iIUF3l5+gIuLjkejF1x/QT6v5XJKnU1FSd12FmZgZnZ+dc9VvKS9vChIGKiIgM4/hx1bO3d54Wq1+/PgAgPDy8yHRML0reffddDB8+HMOHD0epUqVQunRp/O9//9Psaw8PD0yfPh3BwcFQKBQYOHAgAODYsWNo2rQpLCws4ObmhhEjRuD58+ea9aakpODLL7+Em5sb5HI5KleujD///BNA1tN4d+7cQfv27WFnZwcrKyu888472L59e7ZtAWDdunV45513IJfL4eHhgVmzZml9Jg8PD3z77bfo378/bGxsUL58eSxatMhQuzBbDFRERGQYv/+ues7F1X2vqlKlCkxNTfHs2TNERkYaoDADEQJ4/lyaRx6D57Jly2BiYoITJ05g3rx5+Omnn/DHH39o3v/hhx/g7e2NsLAwTJw4EREREQgKCkLnzp1x7tw5rFq1CkeOHMHw4cM1y/Tt2xcrV67EvHnzcOnSJSxcuBDW1tbZbn/YsGFISUnBoUOHEBERge+//z7HtmFhYejWrRt69OiBiIgITJkyBRMnTsTSpUu12s2aNQu+vr4IDw/H0KFDMWTIEFy+fDlP+0UnoghISEgQAERCQoLUpRARUW7Z2AgBCDFnTp4XrVGjhgAgtmzZYoDC9CM5OVlcvHhRJCcnq2Y8e6b6vFI8nj3Ldd3NmjUTXl5eQqlUauaNHTtWeHl5CSGEcHd3F506ddJapk+fPuKTTz7Rmnf48GFhZGQkkpOTxZUrVwQAERISku029+/fLwCIJ0+eCCFU3++UKVNy1bZXr16iZcuWWm2++OILUb16dc1rd3d38eGHH2peK5VK4ejoKBYsWJDjfsjy/b0iP7mDR6iIiEj/UlKA5GTVdIcOeV68Ro0aAIDz58/rsyp6qUGDBlp9lPz9/XHt2jXNIJe+rw3CGhYWhqVLl8La2lrzCAoKglKpxK1bt3DmzBkYGxujWbNmudr+iBEjMH36dDRq1AiTJ0/GuXPncmx76dIlNGrUSGteo0aNtOoFgJo1a2qmZTIZnJ2dERsbm6t69IG3niEiIv07dAhIT1dNe3jkeXF1oAoLC9NjUQZmaQk8eybdtvXIyspK67VSqcSgQYMwYsSILG3Lly+P69ev52n9H3/8MYKCgrBt2zbs3r0bM2bMwKxZs/Dpp59maSuEyNJBXWRzivP128fIZDIoX731kYExUBERkf69emQpH1drqa/0K1KBSiYDXgsihdVx9QUDr7yuXLkyjI2Ns21ft25dXLhwAZUqVcr2/Ro1akCpVOLgwYNo0aJFrmpwc3PD4MGDMXjwYIwbNw6///57toGqevXqOHLkiNa8Y8eOoUqVKjnWKwWe8iMiIv3bulX1PGRIvhb38fEBANy6dQtxcXH6qopeunv3LsaMGYMrV65gxYoV+PnnnzFy5Mgc248dOxahoaEYNmwYzpw5g2vXrmHz5s2aAOTh4YF+/fqhf//+2LhxI27duoUDBw5g9erV2a5v1KhR2LVrF27duoXTp09j37598PLyyrbtZ599hr179+Lrr7/G1atXsWzZMvzyyy/4/PPPdd8ResRARURE+rdvn+rZzi5fi5cqVQpVqlQBwBHTDaFv375ITk5G/fr1MWzYMHz66af45JNPcmxfs2ZNHDx4ENeuXUOTJk1Qp04dTJw4ES6vjC+2YMECdO3aFUOHDkW1atUwcOBArWEVXpWRkYFhw4bBy8sLrVu3RtWqVTF//vxs29atWxerV6/GypUr4e3tjUmTJmHatGkIDg7WaR/om0xkdyKykElMTIRCoUBCQgJsbW2lLoeIiN4kJQUwN1dNX74MVK2ar9X07dsXy5cvx5QpUzB58mQ9FqgfL168wK1bt+Dp6Qlz9ectAt59913Url0bc+bMkboUSb3p+8tP7uARKiIi0i/1FVulSwMvjzLlR506dQAAERER+qiKyKAYqIiISL/++0/1XL9+vjqkq6mv9Dt79qw+qiIyKF7lR0RE+qW+B9/LW8jkV+3atQEA169fx6NHj1C6dGkdCyNAdWsX0j8eoSIiIv1SH6Hy89NpNQ4ODqhcuTIA4NChQ7pWRWRQDFRERKQ/8fHAlSuq6ZdjSelCPfL23r17dV4XkSExUBERkf6Ehqqe7e0BBwedV9e2bVsAQEhIiM7rIjIkBioiItKf335TPT9+rJfVqY9QXb16FfHx8XpZJ5EhMFAREZH+bN6sem7fXi+rs7e3h7u7OwAgPDxcL+skMgQGKiIi0h/1WNGurnpbpfo2NKdOndLbOon0jYGKiIj0IyEhc7plS72tVj18QpG6UTJpmTJliuZ7BIDg4GB06tRJsnoMgYGKiIj049XA06WL3lZb/+V4VqdPn9bbOon0jYGKiIj0Y9061XOrVnpdbb2Xwy9cu3YNjx490uu6CUhNTZW6hGKBgYqIiPRj/nzVc0aGXldrb2+PSpUqAeBRKn149913MXz4cIwZMwYODg5o2bIlLl68iLZt28La2hpOTk7o06cP4uLiNMsolUp8//33qFSpEuRyOcqXL49vvvlG8/7YsWNRpUoVWFpaokKFCpg4cSLS0tKk+HiS4a1niIhIP4yNVWFq5Ei9r7pOnTq4fv06wsPD0VKP/bP0SQiBpKQkSbZtaWkJWR7um7hs2TIMGTIER48exePHj9GsWTMMHDgQs2fPRnJyMsaOHYtu3bph3759AIBx48bh999/x08//YTGjRsjOjoaly9f1qzPxsYGS5cuhaurKyIiIjBw4EDY2Njgyy+/1PtnLawYqIiISHcxMZlHppo00fvqa9eujTVr1mDXrl2F9o90UlISrK2tJdn2s2fPYGVllev2lSpVwsyZMwEAkyZNQt26dfHtt99q3l+8eDHc3Nxw9epVuLi4YO7cufjll1/Qr18/AEDFihXRuHFjTfv//e9/mmkPDw989tlnWLVqVaH9rgyBgYqIiHRXq1bmdKlSel99u3btMGHCBBw5cgTPnj2TLLgUF76+vprpsLAw7N+/P9t9euPGDcTHxyMlJQWBgYE5rm/t2rWYM2cOrl+/jmfPniE9PR22trYGqb2wYqAiIiLdxcYadPU1a9aEnZ0dnjx5gqtXr6Ju3boG3V5+WFpa4tmzZ5JtOy9ePZqlVCrRvn17fP/991naubi44ObNm29c1/Hjx9GjRw9MnToVQUFBUCgUWLlyJWbNmpWnmoo6BioiItKdtTXw7BnQs6fBNlGrVi0cOHAA4eHhhTJQyWSyPJ12Kyzq1q2LdevWwcPDAyYmWWNB5cqVYWFhgb179+Ljjz/O8v7Ro0fh7u6OCRMmaObduXPHoDUXRrzKj4iIdJOWltl/auJEg21GPR4VR0zXr2HDhuHx48fo2bMn/vvvP9y8eRO7d+9G//79kZGRAXNzc4wdOxZffvkl/vrrL9y4cQPHjx/Hn3/+CUDVHysyMhIrV67EjRs3MG/ePGzYsEHiT1XwGKiIiEg3x44Bycmq6apVDbaZOnXqAFCdYiL9cXV1xdGjR5GRkYGgoCB4e3tj5MiRUCgUMDJSxYSJEyfis88+w6RJk+Dl5YXu3bsj9uVp3o4dO2L06NEYPnw4ateujWPHjmGiAYN1YSUTQn3jpcIrMTERCoUCCQkJJa6TGxFRoffVV8D33wMODsDDhwbbTHR0NFxf3iPwxYsXkMvlBttWbrx48QK3bt2Cp6cnzM3NJa2F8u5N319+cgePUBERkW7UnZYN2H8KAJydnTWdr69fv27QbRHlFQMVERHpZs0a1XOHDgbdjEwmg5eXFwDg0qVLBt0WUV4xUBERUf7dv585/fKee4bk4+MDADh58qTBt0WUFwxURESUf//9p3quWRNQKAy+OfWAlLzSjwobBioiIsq/hQtVzy+HNDA0f39/AEBoaChevHhRINt8myJwbRdlQ9/fGwMVERHlX3y86vnl1XeGVr16dbi5uSE5ORk7d+4skG3mxNTUFAAkuyEy6SY1NRUAYGxsrJf1caR0IiLKn5QU4OxZ1XTv3gWySSMjI7Ru3Rq///47Tp48iU6dOhXIdrNjbGyMUqVKacZjsrS0hEwmk6weyj2lUomHDx/C0tIy29Hh84OBioiI8icsTBWqypQBKlcusM2qB/g8ffp0gW0zJ87OzgCgCVVUdBgZGaF8+fJ6C8EMVERElD9Hj6qeGzUCCvDIjPo+fqdPn4YQQtKjQjKZDC4uLnB0dERaWppkdVDemZmZaUaC1wcGKiIiyp9XA1UBqlmzJoyNjREbG6s1erqUjI2N9dYXh4omdkonIqK8EwLYtEk1XcCBysLCAtWqVQNQOE77EQEMVERElB+XL2dOvzwFV5BePe1HVBgwUBERUd6FhGROS3CTYnWgCg8PL/BtE2WHgYqIiPJu5UrVc+nSkmyeR6iosGGgIiKivIuKUj1PmiTJ5mvXrg0AiIyMxKNHjySpgehV+QpU8+fPh6enJ8zNzeHj44PDhw/narmjR4/CxMRE84tARERFUHo6cOeOarp5c0lKsLW1RaVKlQCo/rYQSS3PgWrVqlUYNWoUJkyYgPDwcDRp0gRt2rRBZGTkG5dLSEhA3759ERgYmO9iiYioEIiIyJz28pKsjCZNmgAA9u7dK1kNRGp5DlSzZ8/GgAED8PHHH8PLywtz5syBm5sbFixY8MblBg0ahF69emlubElEREWU+ohQ69aAhGMvtWvXDgADFRUOeQpUqampCAsLQ6tWrbTmt2rVCseOHctxuSVLluDGjRuYPHlyrraTkpKCxMRErQcRERUS48ernn18JC2jXr16AIDLly/j+fPnktZClKdAFRcXh4yMDDg5OWnNd3JyQkxMTLbLXLt2DV999RX++eefXN+AcMaMGVAoFJqHm5tbXsokIiJDEQJ4+lQ1/fKeelIpV64cypcvj4yMjDf+p56oIOSrU/rr903K6V5KGRkZ6NWrF6ZOnYoqVarkev3jxo1DQkKC5nH37t38lElERPqm7owOAG3aSFcHVH+LmjVrBgA4ePCgpLUQ5elefg4ODjA2Ns5yNCo2NjbLUSsAePr0KU6dOoXw8HAMHz4cAKBUKiGEgImJCXbv3o3m2VwhIpfLIZdgoDgiInqLQ4dUz35+gKWltLUAaNasGZYvX44DBw5IXQqVcHk6QmVmZgYfHx+EvDpCLoCQkBA0bNgwS3tbW1tERETgzJkzmsfgwYNRtWpVnDlzBn5+frpVT0REBUs9TE7TptLW8dK7774LAPjvv//Y35YklacjVAAwZswY9OnTB76+vvD398eiRYsQGRmJwYMHA1Cdrrt//z7++usvGBkZwdvbW2t5R0dHmJubZ5lPRERFgPoI1cshC6RWoUIFlCtXDvfu3UNERAQaFfCNmonU8hyounfvjkePHmHatGmIjo6Gt7c3tm/fDnd3dwBAdHT0W8ekIiKiIujBA+DqVUAmAxo3lroaAKp+VDVr1sS9e/cQFhbGQEWSkQkhhNRFvE1iYiIUCgUSEhJga2srdTlERCXTunVA165AjRrAuXNSV6MxZcoUTJ06FZ07d8a6deukLoeKgfzkDt7Lj4iIckd9uq+Q9J9Sa/zyaNmBAwdQBI4RUDHFQEVERLmj7pBeSPpPqTVu3BgymQyPHz/GvXv3pC6HSigGKiIieruEBODMGdV0IQtU5ubmeOeddwAAJ0+elLgaKqkYqIiI6O2OHVONkl6xIuDqKnU1WaiH7uGI6SQVBioiInq7779XPUt8u5mcNHl51OzIkSMSV0IlFQMVERG9nfrWLi9PrRU26o7pYWFhvFEySYKBioiI3uzJk8zpIUOkq+MN3N3dUaZMGaSnp+Py5ctSl0MlEAMVERG92av9krK5b2thoB7gEwBCQ0MlroZKIgYqIiJ6s59+Uj2/DCyFVYMGDQAA5wrRoKNUcjBQERHRm+3dq3p+GVgKK/U9Ys+oh3cgKkAMVERElLPExMzpMWOkqyMX/Pz8IJPJcPLkSVy9elXqcqiEYaAiIqKcqfsjyeVA1arS1vIWnp6eaPrytjg7duyQuBoqaRioiIgoZ+pxnbp1k7aOXHrvvfcAAHv27JG4EippGKiIiChn+/ernps1k7aOXGrRogUA1Y2S09LSJK6GShIGKiIiyt6zZ8CJE6rpwEBpa8mlWrVqwcHBAc+ePcMJde1EBYCBioiIsnf4MJCeDnh6Ah4eUleTK0ZGRgh8Gf72qq9OJCoADFRERJS9fftUz82bS1tHHvn5+QEAj1BRgWKgIiKi7Kn7TwUESFtHHtWrVw8AB/ikgsVARUREWcXFAWFhqukidoSqevXqAID79+8j8dVxtIgMiIGKiIiy+uefzGkXF+nqyAd7e3tUqlQJABASEiJxNVRSMFAREVFW48ernl+O61TUtG7dGgCwc+dOiSuhkoKBioiItKWmAklJqukvvpC2lnzq1KkTAGDTpk0QQkhbDJUIDFRERKRNPTo6ADRpIl0dOmjcuDHkcjkePnyIy5cvS10OlQAMVEREpG3rVtVzv36AUdH8MyGXyzX39eNpPyoIRfM3hYiIDEMIYMMG1XSHDtLWoqM2bdoAALZv3y5xJVQSMFAREVGm8+eB27dV00FBkpaiq7Zt2wIADh06hGfPnklcDRV3DFRERJRp8eLMaSsr6erQgypVqsDT0xOpqanYqj6NSWQgDFRERJTp4kXV88iR0tahBzKZDEEvj7Jt2bJF4mqouGOgIiIilYSEzNvNDBkibS160q9fPwDA+vXr8fz5c4mroeKMgYqIiFS2bQPS0gAvL6BqVamr0Qs/Pz9UqFABL168wObNm6Uuh4oxBioiIlJRX933/vvS1qFHMpkM3bt3BwD2oyKDYqAiIiIgORnYsUM1XYwCFZB5G5pdu3ZBqVRKXA0VVwxUREQE7NkDPH8OuLkBPj5SV6NX/v7+sLa2xqNHjxAeHi51OVRMMVAREVHm6b5OnQCZTNJS9M3U1BStWrUCAKxdu1biaqi4YqAiIirp0tMBdYftYna6T61nz54AgDVr1khcCRVXDFRERCXd4cPAo0dA6dJF9mbIb9OiRQvIZDLcuHED9+7dk7ocKoYYqIiISjr16b727QETE2lrMZBSpUqhdu3aAIDQ0FBpi6FiiYGKiKgkUyqB9etV0507S1uLgTVs2BAAEBISInElVBwxUBERlWQ7dwL376umX3bcLq7at28PANi9e7fElVBxxEBFRFSS/fab6tnLC5DLpa3FwBo0aAAAuHPnDp48eSJxNVTcMFAREZVUSiWg7k80bpy0tRQAhUKBypUrAwAOHTokcTVU3DBQERGVVPv2AQ8fAgoF0LWr1NUUiKCgIAC8DQ3pHwMVEVFJtXq16rlbN8DCQtpaCkjbtm0BqG5DI4SQuBoqThioiIhKouTkzED1ctDLkqBZs2awsrLC3bt3cezYManLoWKEgYqIqCRasABISADs7IBmzaSupsBYWlri/ZejwW9Qj79FpAcMVEREJdFnn6meW7QAjErWnwL1aT+OR0X6VLJ+i4iICDh3LnP6xx+lq0MigYGBkMlkOHfuHO7evSt1OVRMMFAREZU0EydmTpcvL10dEnF0dNSMmr5jxw6Jq6HigoGKiKgkSUgANm9WTasH9SyB1MMncNR00hcGKiKikmTu3MzpAQOkq0NirVu3BqA6QvXixQuJq6HigIGKiKikyMgAli1TTY8aBRgbS1qOlHx8fFCuXDkkJSVh586dUpdDxQADFRFRSbF+PXDzJlC6NDB9utTVSMrIyAhdunQBAGzbtk3iaqg4YKAiIioJhAB++EE1PWwYYGUlbT2FQMuWLQEA+/fvl7gSKg4YqIiISoLly4GTJ1XTw4dLW0sh0aRJExgbG+PGjRuIjIyUuhwq4hioiIhKgk8/VT0HBABlykhbSyFha2sLX19fADxKRbpjoCIiKu4OHAASE1XTS5ZIWkph07x5cwDAvn37JK6EijoGKiKi4kwIYPx41fTQoYC7u7T1FDLqQLV//34IISSuhooyBioiouLs77+B0FDV9P/+J20thVDDhg1hamqKu3fv4saNG1KXQ0UYAxURUXGVkgL07aua/vBDwMVF2noKIUtLS/j7+wPgaT/STb4C1fz58+Hp6Qlzc3P4+Pjg8OHDObZdv349WrZsiTJlysDW1hb+/v7YtWtXvgsmIqJcUg+TIJMBP/8sbS2F2Kun/YjyK8+BatWqVRg1ahQmTJiA8PBwNGnSBG3atMnxktNDhw6hZcuW2L59O8LCwhAQEID27dsjPDxc5+KJiCgHt25lDt75999AqVKSllOYBQQEAGA/KtKNTOTxp8fPzw9169bFggULNPO8vLzQqVMnzJgxI1freOedd9C9e3dMmjQpV+0TExOhUCiQkJAAW1vbvJRLRFTyCAF06ABs3Qo0bw7s2aM6SkXZSklJgZ2dHZKTk3HhwgVUr15d6pJIYvnJHXk6QpWamoqwsDC0atVKa36rVq1w7NixXK1DqVTi6dOnsLe3z7FNSkoKEhMTtR5ERJRLGzaowpSpKfDLLwxTbyGXy9G4cWMAwPLlyyWuhoqqPAWquLg4ZGRkwMnJSWu+k5MTYmJicrWOWbNm4fnz5+jWrVuObWbMmAGFQqF5uLm55aVMIqKSKzYW+OQT1fQXXwBeXtLWU0T06tULADBv3jw8f/5c4mqoKMpXp3TZa//bEUJkmZedFStWYMqUKVi1ahUcHR1zbDdu3DgkJCRoHnfv3s1PmUREJYtSCQQHA48eAbVqARMnSl1RkdGvXz9UqFABSUlJ2LBhg9TlUBGUp0Dl4OAAY2PjLEejYmNjsxy1et2qVaswYMAArF69Gi1atHhjW7lcDltbW60HERG9xbRpwI4dgIUFsGwZYG4udUVFhkwm05w54Wk/yo88BSozMzP4+PggJCREa35ISAgaNmyY43IrVqxAcHAw/v33X7z33nv5q5SIiHL211/A1Kmq6d9+Ux2hojz56KOPAAC7d+/GtWvXJK6Gipo8n/IbM2YM/vjjDyxevBiXLl3C6NGjERkZicGDBwNQna7rqx5IDqow1bdvX8yaNQsNGjRATEwMYmJikJCQoL9PQURUkh08CPTrp5ru3h3o00faeoqoKlWqoGXLlgCApUuXSlsMFTl5DlTdu3fHnDlzMG3aNNSuXRuHDh3C9u3b4f7y/lDR0dFaY1L99ttvSE9Px7Bhw+Di4qJ5jBw5Un+fgoiopIqKAl7tRsGbH+vk448/BgD8+++/UCqVEldDRUmex6GSAsehIiLKxoMHQOPGwPXrqtcPHwIODtLWVMQlJyfDyckJT58+xeHDhzXDKVDJYvBxqIiIqJCIiwNatVKFKQ8P4MYNhik9sLCwQOfOnQEAf//9t8TVUFHCQEVEVNQ8eqQ6zXfuHODsDOzeDVSoIHVVxUbv3r0BAMuWLcOjR48kroaKCgYqIqKi5P59VZg6exZwcgL27wcqV5a6qmKlefPmqFSpEl68eIGFCxdKXQ4VEQxURERFxdmzgJ8fcOYM4OgI7NsHVKsmdVXFjrGxMf73v/8BAH799VckJydLXBEVBQxURERFwfbtQKNGqiNUXl7AsWMAb+JrMD179kT58uURHR2NRYsWSV0OFQEMVEREhd3cuUD79sDz50BgIHD0KFCxotRVFWtmZmYYN24cAODHH39ESkqKxBVRYcdARURUWD1/DgwbBowapbpP34ABqiNVdnZSV1YiBAcHw9XVFffu3cPPP/8sdTlUyDFQEREVRmfPAr6+wPz5qtfffAP8/jtgZiZtXSWIubk5JkyYAACYM2cO0tLSJK6ICjMGKiKiwkSpBObMAerXBy5fBlxcgF27gPHjAZlM6upKnAEDBsDZ2Rn379/Hv//+K3U5VIgxUBERFRY3bgB16gCjRwOpqap+U+fOqQbwJEnI5XIMGzYMAPDzzz+jCNxchCTCQEVEJLWUFGD6dMDbWxWgTE1Vp/o2beLo54XAgAEDIJfLERYWhtWrV0tdDhVSDFRERFI6cACoXRuYOBF48UJ1qu/kSWDIEJ7iKyRcXFwwduxYAMDs2bMlroYKKwYqIiIp3L4N9O4NBASo+ko5OQH//gscPw7UqiV1dfSaIUOGwMTEBP/99x+OHTsmdTlUCDFQEREVpBs3gL59Vffe+/df1VGooUNVoapnTx6VKqScnZ0RHBwMAJg8ebK0xVChxEBFRGRoQgCHDwNdugBVqgDLl6vmtWihOr33669AqVJSV0lvMX78eMhkMuzZsweRkZFSl0OFDAMVEZGhvHihCk++vkDTpsD69aphEdq0AU6dAkJCAB8fqaukXPL09IS/vz8AYPv27RJXQ4UNAxURkT4JAYSHq4Y+cHFRnd47fRowNwc++QQ4f1412jmDVJHUqVMnAMCCBQukLYQKHZkoAoNqJCYmQqFQICEhAba2tlKXQ0SU1aVLwF9/ARs2AFeuZM53c1NdsTdwIIdAKAYePXoEZ2dnpKen4+LFi/Dy8pK6JDKA/OQOEwPXRERUPKWmAidOAHv2ADt2qPpCqZmbA++9B/TvD7RuDRjxZEBxUbp0abRq1Qrbt2/H5s2bGahIg4GKiCg3lEogIkIVoPbuBQ4dUt28WM3YGGjbFujVS/XMo+nFVvPmzbF9+3asW7cOX375JWS8MpPAU35ERNlLT1fdoPjwYeDIEVWAevhQu02ZMkBgoOrRvr1qLCkq9mJiYlChQgUkJyfj119/xdChQ6UuifQsP7mDgYqICFCFpf/+U3UgP3IECA0Fnj7VbmNlBTRrphruIDBQdasYns4rkX766SeMGTMGpqamOHz4MPz8/KQuifSIgYqI6G1SUoBr11T3zIuIUD2fPQvcv5+1rUIBNGoENG4MNGmiui2MmVnB10yFTnp6Otq3b4+dO3fC09MT58+fh6WlpdRlkZ6wUzoREaA6XRcZCVy/nvm4dk119d2NG6r+UNmpWhWoVw/w81MFKG9vVd8ooteYmJhg+fLlqF69Om7duoWvvvoK8+bNk7oskhCPUBFR0ZKWpjo9Fx0N3LsH3L2r/Xznjuo5IyPnddjYADVqZD5q1VI9bGwK7nNQsbBhwwZ07twZALBv3z4EBARIXBHpA0/5EVHRI4Sqr9KTJ0BcnCosPXwIxMYCMTFZH3FxuVuvXA5UrAhUqqR6VKwIVKumOgrl6sp75pHe9OnTB3///TeaN2+OPXv28Kq/YoCBiogKhhCqcZieP898PHumCkbqx+uv1Y8nT7Qf8fFvPpqUHWNjwNERKFdO9XBzy3wuXx7w8ACcndlhnArEzZs38c477+DFixe86q+YYKAi0ofsfiXyMk+pzHx+ffpN7+U0nZGh6hOUnq49nd1r9SMtLedHamr281NSVPeee/EiczolBUhOVj2SkjKnk5Nz7oeUX2ZmqmEIHBxUz46OqlD0+sPJCShdmn2bqFCZO3cuRo0aBQsLC0RFRaEUb3ZdpDFQ5dc336ju9v4qXf+oGmpdBTGvsNSR23m6rIt0Z2qqGk7A2lrVB0n9nNOjVCnAzi7zYW+verawkPqTEOWbUqlE1apVcf36dcydOxcjRoyQuiTSAa/yy6/ERFUHV6KCJpOpTkupn7ObNjFRPYyNM6dff21srAo26tempm9/mJlpv5bLVbdMMTfPnJbLVUEnu4elpSpImZpKvReJJGdkZIRRo0Zh+PDhmDBhArp16wZnZ2epy6ICxCNUABAVlXUEZCD7TquFaV5hq6cozjPEOtVh6E1BSf2aiIqNlJQUeHl54datW+jatSvWrFkjdUmUTzzlR0REJKHQ0FA0bNgQxsbGuHnzJsqXLy91SZQP+ckdvASGiIhIT/z9/REQEICMjAz06dMHKSkpUpdEBYSBioiISI9+/fVX2Nra4tChQ+jduzeU+r4ilgolBioiIiI98vLywtq1a2FsbIx169Zh4cKFUpdEBYCBioiISM9atmyJH374AQDwxRdf4NChQxJXRIbGQEVERGQAI0aMgL+/P5KSktC2bVvcvHlT6pLIgBioiIiIDMDY2Bjbtm2Dr68vnj9/jtatW+PixYtSl0UGwkBFRERkIHZ2dli/fj1cXV1x7do11KtXDz/99JPUZZEBMFAREREZkJubG8LCwlC5cmUkJSVhzJgx2Lp1q9RlkZ4xUBERERmYs7MzLl68iI4dOwIAunTpgmXLlklcFekTAxUREVEBMDExwerVq/H+++8jNTUV/fv3x9mzZ6Uui/SEgYqIiKiAmJmZYfXq1Xj33XehVCrRo0cPJCYmSl0W6QEDFRERUQEyMTHBypUr4ejoiMuXL6Nt27aIi4uTuizSEQMVERFRAXNycsKOHTtQqlQpHD16FLVr18bJkyelLot0wEBFREQkgbp16+LgwYOoXLky7t+/jyZNmmD9+vVSl0X5xEBFREQkkZo1a+LYsWMICgpCSkoKunbtim3btkldFuUDAxUREZGEHBwcsHXrVnTq1AlCCHTv3h2LFy+GEELq0igPGKiIiIgkpu6oHhgYiOfPn2PAgAEICgrChQsXpC6NcomBioiIqBCQy+XYsmULpkyZAlNTU4SEhKB27do8BVhEMFAREREVEhYWFpg8eTKOHz+OOnXqID09HR06dMCUKVPw4MEDqcujN2CgIiIiKmTq1q2LI0eOoFGjRlAqlZg6dSqcnZ0xatQojllVSDFQERERFUKWlpbYv38//vjjD9jZ2QEA5s6dC3d3dwwaNAjXrl2TuEJ6FQMVERFRIWVqaooBAwYgKioKK1euRPXq1ZGUlIRFixahWrVq6N69O06fPi11mQQGKiIiokLP3Nwc3bt3x/nz53HgwAG89957UCqVWL16NXx8fNC7d29cvHhR6jJLNAYqIiKiIkImk6FZs2bYunUrzp49i169egEA/v33X/j5+WH69OkICwtDamqqxJWWPDJRBEYOS0xMhEKhQEJCAmxtbaUuh4iIqNA4fvw4Pv/8cxw9elQzz9zcHB999BHat2+PypUrw8PDAyYmJhJWWbTkJ3cwUBERERVxqampWLx4MbZv345Dhw4hISFB630zMzO0adMGH374Ifz8/ODm5iZRpUUDAxUREVEJJ4TAypUrsXLlSty6dQvXrl3DixcvtNpUrlwZgwYNQvPmzeHt7Q1TU1OJqi2c8pM78tWHav78+fD09IS5uTl8fHxw+PDhN7Y/ePAgfHx8YG5ujgoVKmDhwoX52SwRERG9hUwmQ8+ePbFp0yacO3cOz58/R0REBL744gs4OzsDAK5du4bPP/8cdevWhY2NDRo0aIDhw4dj+fLliImJkfgTFE15PkK1atUq9OnTB/Pnz0ejRo3w22+/4Y8//sDFixdRvnz5LO1v3boFb29vDBw4EIMGDcLRo0cxdOhQrFixAl26dMnVNnmEioiISD/u37+PZcuWYf/+/Th16hTi4+OztHFwcEDHjh1Rs2ZN1KpVC1WrVkWZMmVgbGxc8AVLoEBO+fn5+aFu3bpYsGCBZp6Xlxc6deqEGTNmZGk/duxYbN68GZcuXdLMGzx4MM6ePYvQ0NBcbZOBioiISP+EELhx4wZOnTqFkydPYseOHVp/r19lZGQEJycnuLm5wcnJCXZ2dihVqhRKlSqFsmXLomLFinB3d4elpSVMTExgamoKU1NTzXRRCmMGD1SpqamwtLTEmjVr8P7772vmjxw5EmfOnMHBgwezLNO0aVPUqVMHc+fO1czbsGEDunXrhqSkpGzP26akpCAlJUXrg7m5uTFQERERGZAQAmfOnMH58+dx/fp1nD17FmfOnMHdu3ehVCp1WrdMJtMKWq+GLZlMpnkYGRlpvX79oX7f398fixYt0tMn15afQJWnayjj4uKQkZEBJycnrflOTk45nnONiYnJtn16ejri4uLg4uKSZZkZM2Zg6tSpeSmNiIiIdCSTyVCnTh3UqVNHa35GRgYePnyI+/fv4+7du4iNjUV8fDzi4+Px5MkTREZG4saNG7h79y5SUlKQkZGRZd1CCKSlpSEtLU0vtar7gxUW+RqUQiaTab0WQmSZ97b22c1XGzduHMaMGaN5rT5CRURERAXP2NgYzs7OcHZ2ho+Pz1vbCyGQnp6OtLQ0zfObpoUQmodSqdR6ndM89f0NC4s8BSoHBwcYGxtnORoVGxub5SiUmrOzc7btTUxMULp06WyXkcvlkMvleSmNiIiICgmZTKY5rVdS5GnYBDMzM/j4+CAkJERrfkhICBo2bJjtMv7+/lna7969G76+viVqRxMREVHxledxqMaMGYM//vgDixcvxqVLlzB69GhERkZi8ODBAFSn6/r27atpP3jwYNy5cwdjxozBpUuXsHjxYvz555/4/PPP9fcpiIiIiCSU5z5U3bt3x6NHjzBt2jRER0fD29sb27dvh7u7OwAgOjoakZGRmvaenp7Yvn07Ro8ejV9//RWurq6YN29ersegIiIiIirseOsZIiIiolcU2K1niIiIiCgTAxURERGRjhioiIiIiHTEQEVERESkIwYqIiIiIh0xUBERERHpiIGKiIiISEcMVEREREQ6YqAiIiIi0hEDFREREZGO8nwvPymo746TmJgocSVERERU3KnzRl7uzlckAtXTp08BAG5ubhJXQkRERCXF06dPoVAoctW2SNwcWalUIioqCjY2NpDJZFKX81aJiYlwc3PD3bt3eTPnfOD+yz/uO91w/+mG+y//uO90o+/9J4TA06dP4erqCiOj3PWOKhJHqIyMjFCuXDmpy8gzW1tb/mLogPsv/7jvdMP9pxvuv/zjvtONPvdfbo9MqbFTOhEREZGOGKiIiIiIdMRAZQByuRyTJ0+GXC6XupQiifsv/7jvdMP9pxvuv/zjvtNNYdh/RaJTOhEREVFhxiNURERERDpioCIiIiLSEQMVERERkY4YqIiIiIh0xECVCykpKahduzZkMhnOnDmj9V5kZCTat28PKysrODg4YMSIEUhNTdVqExERgWbNmsHCwgJly5bFtGnTstwf6ODBg/Dx8YG5uTkqVKiAhQsXZqlj3bp1qF69OuRyOapXr44NGzbo/bPqS4cOHVC+fHmYm5vDxcUFffr0QVRUlFYb7rvs3b59GwMGDICnpycsLCxQsWJFTJ48Ocu+4f7L2TfffIOGDRvC0tISpUqVyrYN959+zZ8/H56enjA3N4ePjw8OHz4sdUkGd+jQIbRv3x6urq6QyWTYuHGj1vtCCEyZMgWurq6wsLDAu+++iwsXLmi1SUlJwaeffgoHBwdYWVmhQ4cOuHfvnlabJ0+eoE+fPlAoFFAoFOjTpw/i4+O12uTm57kwmTFjBurVqwcbGxs4OjqiU6dOuHLlilabIrf/BL3ViBEjRJs2bQQAER4erpmfnp4uvL29RUBAgDh9+rQICQkRrq6uYvjw4Zo2CQkJwsnJSfTo0UNERESIdevWCRsbG/Hjjz9q2ty8eVNYWlqKkSNHiosXL4rff/9dmJqairVr12raHDt2TBgbG4tvv/1WXLp0SXz77bfCxMREHD9+vED2QV7Nnj1bhIaGitu3b4ujR48Kf39/4e/vr3mf+y5nO3bsEMHBwWLXrl3ixo0bYtOmTcLR0VF89tlnmjbcf282adIkMXv2bDFmzBihUCiyvM/9p18rV64Upqam4vfffxcXL14UI0eOFFZWVuLOnTtSl2ZQ27dvFxMmTBDr1q0TAMSGDRu03v/uu++EjY2NWLdunYiIiBDdu3cXLi4uIjExUdNm8ODBomzZsiIkJEScPn1aBAQEiFq1aon09HRNm9atWwtvb29x7NgxcezYMeHt7S3atWuneT83P8+FTVBQkFiyZIk4f/68OHPmjHjvvfdE+fLlxbNnzzRtitr+Y6B6i+3bt4tq1aqJCxcuZAlU27dvF0ZGRuL+/fuaeStWrBByuVwkJCQIIYSYP3++UCgU4sWLF5o2M2bMEK6urkKpVAohhPjyyy9FtWrVtLY7aNAg0aBBA83rbt26idatW2u1CQoKEj169NDbZzWkTZs2CZlMJlJTU4UQ3Hd5NXPmTOHp6al5zf2XO0uWLMk2UHH/6Vf9+vXF4MGDteZVq1ZNfPXVVxJVVPBeD1RKpVI4OzuL7777TjPvxYsXQqFQiIULFwohhIiPjxempqZi5cqVmjb3798XRkZGYufOnUIIIS5evCgAaAXw0NBQAUBcvnxZCJG7n+fCLjY2VgAQBw8eFEIUzf3HU35v8ODBAwwcOBDLly+HpaVllvdDQ0Ph7e0NV1dXzbygoCCkpKQgLCxM06ZZs2Zag40FBQUhKioKt2/f1rRp1aqV1rqDgoJw6tQppKWlvbHNsWPH9PJZDenx48f4559/0LBhQ5iamgLgvsurhIQE2Nvba15z/+mG+09/UlNTERYWluUztmrVqth8xvy4desWYmJitPaLXC5Hs2bNNPslLCwMaWlpWm1cXV3h7e2taRMaGgqFQgE/Pz9NmwYNGkChUGi1edvPc2GXkJAAAJp/54ri/mOgyoEQAsHBwRg8eDB8fX2zbRMTEwMnJyeteXZ2djAzM0NMTEyObdSv39YmPT0dcXFxb2yjXkdhNHbsWFhZWaF06dKIjIzEpk2bNO9x3+XejRs38PPPP2Pw4MGaedx/uuH+05+4uDhkZGQU68+YH+rP/qb9EhMTAzMzM9jZ2b2xjaOjY5b1Ozo6vvHn8PWf58JMCIExY8agcePG8Pb2BlA091+JC1RTpkyBTCZ74+PUqVP4+eefkZiYiHHjxr1xfTKZLMs8IYTW/NfbiJedWvXRJrvtG0pu953aF198gfDwcOzevRvGxsbo27evVofekrTvgLzvPwCIiopC69at8cEHH+Djjz/Weo/77+37701K2v4ztJLwGfMjP/vlbT+H+W1TWA0fPhznzp3DihUrsrxXlPafSa5bFhPDhw9Hjx493tjGw8MD06dPx/Hjx7PcF8jX1xe9e/fGsmXL4OzsjBMnTmi9/+TJE6SlpWnSrrOzc5aEGxsbCwBvbWNiYoLSpUu/sc3rqdqQcrvv1BwcHODg4IAqVarAy8sLbm5uOH78OPz9/UvcvgPyvv+ioqIQEBAAf39/LFq0SKsd91/2Xt1/b1IS95+hODg4wNjYuFh/xvxwdnYGoDr64eLiopn/6n5xdnZGamoqnjx5onWUJTY2Fg0bNtS0efDgQZb1P3z4UGs9b/t5Lqw+/fRTbN68GYcOHUK5cuU084vk/st1b6sS5s6dOyIiIkLz2LVrlwAg1q5dK+7evSuEyOzIFhUVpVlu5cqVWTq2lipVSqSkpGjafPfdd1k6tnp5eWltf/DgwVk6trZp00arTevWrYtMx9bIyEgBQOzfv18IwX33Nvfu3ROVK1cWPXr00LpaRY37L3fe1imd+08/6tevL4YMGaI1z8vLi53SnZ3F999/r5mXkpKSbafqVatWadpERUVl26n6xIkTmjbHjx/PtlP1m36eCxulUimGDRsmXF1dxdWrV7N9v6jtPwaqXLp161aOwyYEBgaK06dPiz179ohy5cppXWoZHx8vnJycRM+ePUVERIRYv369sLW1zfbS69GjR4uLFy+KP//8M8ul10ePHhXGxsbiu+++E5cuXRLfffddob30+sSJE+Lnn38W4eHh4vbt22Lfvn2icePGomLFiporprjvcnb//n1RqVIl0bx5c3Hv3j0RHR2teahx/73ZnTt3RHh4uJg6daqwtrYW4eHhIjw8XDx9+lQIwf2nb+phE/78809x8eJFMWrUKGFlZSVu374tdWkG9fTpU83PFgAxe/ZsER4erhku4rvvvhMKhUKsX79eREREiJ49e2Z72X+5cuXEnj17xOnTp0Xz5s2zvey/Zs2aIjQ0VISGhooaNWpke9n/m36eC5shQ4YIhUIhDhw4oPVvXFJSkqZNUdt/DFS5lF2gEkL1D/d7770nLCwshL29vRg+fLjWZdZCCHHu3DnRpEkTIZfLhbOzs5gyZYrmf7hqBw4cEHXq1BFmZmbCw8NDLFiwIEsNa9asEVWrVhWmpqaiWrVqYt26dXr/nPpw7tw5ERAQIOzt7YVcLhceHh5i8ODB4t69e1rtuO+yt2TJEgEg28eruP9y1q9fv2z3n/oIqRDcf/r266+/Cnd3d2FmZibq1q2rufy9ONu/f3+2P2f9+vUTQqiOskyePFk4OzsLuVwumjZtKiIiIrTWkZycLIYPHy7s7e2FhYWFaNeunYiMjNRq8+jRI9G7d29hY2MjbGxsRO/evcWTJ0+02uTm57kwyenfuCVLlmjaFLX9J3v5wYiIiIgon0rcVX5ERERE+sZARURERKQjBioiIiIiHTFQEREREemIgYqIiIhIRwxURERERDpioCIiIiLSEQMVERERkY4YqIhKsHfffRejRo2SZH363nZeSLVtDw8PzJkzR6d1BAcHo1OnTm9sI+W+JSqpTKQugIiKj/Xr18PU1FTvbfPrwIEDCAgIwJMnT1CqVCmDbouISjYGKiLSG3t7e4O0LQzS0tIMHgCJqOjiKT+iEuL58+fo27cvrK2t4eLiglmzZmVpk5qaii+//BJly5aFlZUV/Pz8cODAAa02R48eRbNmzWBpaQk7OzsEBQXhyZMnALKeapo/fz4qV64Mc3NzODk5oWvXrpr3Xm/75MkT9O3bF3Z2drC0tESbNm1w7do1zftLly5FqVKlsGvXLnh5ecHa2hqtW7dGdHR0tp/39u3bCAgIAADY2dlBJpMhODhY875SqcSXX34Je3t7ODs7Y8qUKVrLy2QyLFy4EB07doSVlRWmT58OANiyZQt8fHxgbm6OChUqYOrUqUhPT9csN2XKFJQvXx5yuRyurq4YMWKE1nqTkpLQv39/2NjYoHz58li0aJHW+xEREWjevDksLCxQunRpfPLJJ3j27Fm2nxHI3fdKRIbHQEVUQnzxxRfYv38/NmzYgN27d+PAgQMICwvTavPRRx/h6NGjWLlyJc6dO4cPPvgArVu31gSbM2fOIDAwEO+88w5CQ0Nx5MgRtG/fHhkZGVm2d+rUKYwYMQLTpk3DlStXsHPnTjRt2jTH+oKDg3Hq1Cls3rwZoaGhEEKgbdu2SEtL07RJSkrCjz/+iOXLl+PQoUOIjIzE559/nu363NzcsG7dOgDAlStXEB0djblz52reX7ZsGaysrHDixAnMnDkT06ZNQ0hIiNY6Jk+ejI4dOyIiIgL9+/fHrl278OGHH2LEiBG4ePEifvvtNyxduhTffPMNAGDt2rX46aef8Ntvv+HatWvYuHEjatSoobXOWbNmwdfXF+Hh4Rg6dCiGDBmCy5cvaz5f69atYWdnh5MnT2LNmjXYs2cPhg8fnuN+y833SkQFQBBRsff06VNhZmYmVq5cqZn36NEjYWFhIUaOHCmEEOL69etCJpOJ+/fvay0bGBgoxo0bJ4QQomfPnqJRo0Y5bqdZs2aa9a1bt07Y2tqKxMTEt7a9evWqACCOHj2qeT8uLk5YWFiI1atXCyGEWLJkiQAgrl+/rmnz66+/Cicnpxzr2b9/vwAgnjx5kmXbjRs31ppXr149MXbsWM1rAGLUqFFabZo0aSK+/fZbrXnLly8XLi4uQgghZs2aJapUqSJSU1Ozrcfd3V18+OGHmtdKpVI4OjqKBQsWCCGEWLRokbCzsxPPnj3TtNm2bZswMjISMTExQggh+vXrJzp27CiEyN33SkQFg0eoiEqAGzduIDU1Ff7+/pp59vb2qFq1qub16dOnIYRAlSpVYG1trXkcPHgQN27cAJB5hCo3WrZsCXd3d1SoUAF9+vTBP//8g6SkpGzbXrp0CSYmJvDz89PMK126NKpWrYpLly5p5llaWqJixYqa1y4uLoiNjc3dTnhNzZo1tV5nty5fX1+t12FhYZg2bZrW/hk4cCCio6ORlJSEDz74AMnJyahQoQIGDhyIDRs2aJ0OfH27MpkMzs7Omu1eunQJtWrVgpWVlaZNo0aNoFQqceXKlSyfITffKxEVDHZKJyoBhBBvbaNUKmFsbIywsDAYGxtrvWdtbQ0AsLCwyPU2bWxscPr0aRw4cAC7d+/GpEmTMGXKFJw8eTLLFXc51SeEgEwm07x+vVO4TCbL1WfLTnbrUiqVWvNeDTaAah9NnToVnTt3zrI+c3NzuLm54cqVKwgJCcGePXswdOhQ/PDDDzh48KBme2/a7uuf9/V2r8vvZyci/eMRKqISoFKlSjA1NcXx48c18548eYKrV69qXtepUwcZGRmIjY1FpUqVtB7Ozs4AVEdX9u7dm+vtmpiYoEWLFpg5cybOnTuH27dvY9++fVnaVa9eHenp6Thx4oRm3qNHj3D16lV4eXnl5yMDAMzMzAAg2z5e+VG3bl1cuXIly/6pVKkSjIxU/5xaWFigQ4cOmDdvHg4cOIDQ0FBERETkav3Vq1fHmTNn8Pz5c828o0ePwsjICFWqVMnSPjffKxEVDB6hIioBrK2tMWDAAHzxxRcoXbo0nJycMGHCBE0IAIAqVaqgd+/e6Nu3L2bNmoU6deogLi4O+/btQ40aNdC2bVuMGzcONWrUwNChQzF48GCYmZlh//79+OCDD+Dg4KC1za1bt+LmzZto2rQp7OzssH37diiVymxPR1WuXBkdO3bEwIED8dtvv8HGxgZfffUVypYti44dO+b7c7u7u0Mmk2Hr1q1o27YtLCwsNEfb8mPSpElo164d3Nzc8MEHH8DIyAjnzp1DREQEpk+fjqVLlyIjIwN+fn6wtLTE8uXLYWFhAXd391ytv3fv3pg8eTL69euHKVOm4OHDh/j000/Rp08fODk5ZWmfm++ViAoGf+uISogffvgBTZs2RYcOHdCiRQs0btwYPj4+Wm2WLFmCvn374rPPPkPVqlXRoUMHnDhxAm5ubgBUoWv37t04e/Ys6tevD39/f2zatAkmJln/b1aqVCmsX78ezZs3h5eXFxYuXIgVK1bgnXfeyba+JUuWwMfHB+3atYO/vz+EENi+fbtOYz+VLVsWU6dOxVdffQUnJ6c3Xi2XG0FBQdi6dStCQkJQr149NGjQALNnz9YEplKlSuH3339Ho0aNNEfztmzZgtKlS+dq/ZaWlti1axceP36MevXqoWvXrggMDMQvv/yS4zK5+V6JyPBkgifhiYiIiHTCI1REREREOmKgIiIiItIRAxURERGRjhioiIiIiHTEQEVERESkIwYqIiIiIh0xUBERERHpiIGKiIiISEcMVEREREQ6YqAiIiIi0hEDFREREZGO/g/QcD1ZA71JBAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 5))\n",
+    "ax.plot(thresholds, prec[:-1], \"r\", label=\"precision\")\n",
+    "ax.plot(thresholds, recall[:-1], \"black\", label=\"recall\")\n",
+    "ax.set_xlabel(\"decision threshold\")\n",
+    "ax.legend()\n",
+    "ax.set_title(\"Precision & Recall vs. Threshold\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "755b033a",
+   "metadata": {},
+   "source": [
+    "**Reading this plot:** as the threshold moves right (more cautious), the red\n",
+    "precision line climbs while the black recall line falls. The crossing point\n",
+    "is roughly where precision and recall are balanced — but you might deliberately\n",
+    "pick a different point depending on what matters more for your use case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e918f729",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(recall, prec)\n",
+    "plt.xlabel(\"recall\")\n",
+    "plt.ylabel(\"precision\")\n",
+    "plt.title(\"Precision vs. Recall\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c55404a3",
+   "metadata": {},
+   "source": [
+    "**Reading this plot:** this is the same information, just recall on the x-axis\n",
+    "and precision on the y-axis directly against each other. The closer the curve\n",
+    "hugs the top-right corner, the better the model — that corner represents\n",
+    "perfect precision AND perfect recall simultaneously (impossible in practice,\n",
+    "but the benchmark to compare models against)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6795b480",
+   "metadata": {},
+   "source": [
+    "### 7.1 Picking a threshold for a target precision\n",
+    "\n",
+    "Say your goal is \"I want at least 80% precision, whatever recall that costs.\"\n",
+    "You can search the threshold array for the first point where precision crosses 0.8:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bfa77eb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thresh_for_80_prec = thresholds[(prec >= 0.8).argmax()]\n",
+    "y_pred_80_prec = (y_scores >= thresh_for_80_prec)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0b56438e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8001991370726851"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(y_train8, y_pred_80_prec)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "1510e738",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.41206631345069217"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(y_train8, y_pred_80_prec)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd92dc6f",
+   "metadata": {},
+   "source": [
+    "Notice precision is now pinned near 0.80 (as requested), but recall has\n",
+    "dropped compared to the default threshold=0 predictions. That's the tradeoff\n",
+    "made concrete — you traded recall to guarantee a higher precision."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b365ae63",
+   "metadata": {},
+   "source": [
+    "## 8. ROC Curve — a different lens on the same tradeoff\n",
+    "\n",
+    "ROC stands for \"Receiver Operating Characteristic\" (a name from old radar\n",
+    "signal detection). Instead of precision vs. recall, it plots:\n",
+    "\n",
+    "* **TPR (True Positive Rate)** = recall — same thing, different name here\n",
+    "* **FPR (False Positive Rate)** = `FP / (FP + TN)` — the fraction of actual\n",
+    "  negatives that get wrongly flagged as positive\n",
+    "\n",
+    "A model with **no skill at all** (random guessing) traces the diagonal line\n",
+    "from (0,0) to (1,1). A perfect model would shoot straight up to (0,1) and\n",
+    "across — catching every positive with zero false alarms. The more the curve\n",
+    "bulges toward the top-left, the better.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "cd94672e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fpr, tpr, thresholds = roc_curve(y_train8, y_scores)\n",
+    "\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.plot([0, 1], [0, 1], \"k--\", label=\"random guessing\")\n",
+    "plt.xlabel(\"FPR\")\n",
+    "plt.ylabel(\"TPR\")\n",
+    "plt.legend()\n",
+    "plt.title(\"ROC Curve — SGD\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "681d7314",
+   "metadata": {},
+   "source": [
+    "## 9. ROC AUC — the whole curve as one number\n",
+    "\n",
+    "AUC = \"Area Under the Curve\". It's literally the area under the ROC curve,\n",
+    "ranging from 0.5 (random guessing — no better than a coin flip) to 1.0\n",
+    "(perfect classifier, no overlap between positive and negative score\n",
+    "distributions at all).\n",
+    "\n",
+    "**Rule of thumb for reading AUC:**\n",
+    "* 0.5 → useless, no better than random\n",
+    "* 0.7–0.8 → acceptable\n",
+    "* 0.8–0.9 → good\n",
+    "* 0.9+ → excellent (common for MNIST since digits are visually distinct)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ff7fc59c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9152748921182394"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roc_auc_score(y_train8, y_scores)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8bb5633",
+   "metadata": {},
+   "source": [
+    "## 10. Random Forest version — same workflow, different model\n",
+    "\n",
+    "Random Forest is a non-linear ensemble of decision trees. It generally\n",
+    "outperforms a linear model like SGD on raw pixel data. The one mechanical\n",
+    "difference: Random Forest has no `decision_function` (it's not drawing a\n",
+    "single linear boundary) — instead it gives **class probabilities** via\n",
+    "`predict_proba`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "109ce298",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False,  True])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf = RandomForestClassifier(random_state=42)\n",
+    "rf.fit(X_train, y_train8)\n",
+    "rf.predict(X_train[-3:])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "499be9f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf.score(X_train, y_train8)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4f1a980c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.97966667, 0.98158333, 0.98241667, 0.97958333, 0.98316667])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cross_val_score(rf, X_train, y_train8, cv=5, scoring=\"accuracy\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1c87b746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_rf = cross_val_predict(rf, X_train, y_train8, cv=5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f25bf5ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9870210135970334"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(y_train8, y_pred_rf)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "59f8d7d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.818834387284225"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(y_train8, y_pred_rf)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "2455cb2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8950957496496964"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_train8, y_pred_rf)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03b2cd66",
+   "metadata": {},
+   "source": [
+    "**`predict_proba` instead of `decision_function`:** it returns, for every\n",
+    "sample, a pair of probabilities `[P(not 8), P(is 8)]` that sum to 1. We take\n",
+    "column index 1 — the probability of being an 8 — and use that exactly like we\n",
+    "used `y_scores` for SGD above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a64ad282",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(60000, 2)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_probas_rf = cross_val_predict(rf, X_train, y_train8, cv=5, method=\"predict_proba\")\n",
+    "y_probas_rf.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "e92e4b97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_scores_rf = y_probas_rf[:, 1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3d5e0b46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prec_rf, recall_rf, thresholds_rf = precision_recall_curve(y_train8, y_scores_rf)\n",
+    "\n",
+    "plt.plot(recall_rf, prec_rf)\n",
+    "plt.xlabel(\"recall\")\n",
+    "plt.ylabel(\"precision\")\n",
+    "plt.title(\"PR Curve — Random Forest\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "dd8decae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_train8, y_scores_rf)\n",
+    "\n",
+    "plt.plot(fpr_rf, tpr_rf)\n",
+    "plt.xlabel(\"FPR\")\n",
+    "plt.ylabel(\"TPR\")\n",
+    "plt.title(\"ROC Curve — Random Forest\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f055f9b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9969179908862156"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roc_auc_score(y_train8, y_scores_rf)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9ae1fb9",
+   "metadata": {},
+   "source": [
+    "### 10.1 Bonus — compare both models on one ROC plot\n",
+    "\n",
+    "Plotting both curves together makes the comparison immediate: whichever\n",
+    "curve bulges further toward the top-left corner is the better classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "53bbf731",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(fpr, tpr, \"b:\", label=\"SGD\")\n",
+    "plt.plot(fpr_rf, tpr_rf, \"r-\", label=\"Random Forest\")\n",
+    "plt.xlabel(\"FPR\")\n",
+    "plt.ylabel(\"TPR\")\n",
+    "plt.legend()\n",
+    "plt.title(\"ROC Comparison — SGD vs Random Forest\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8296dd6a",
+   "metadata": {},
+   "source": [
+    "**Why Random Forest usually wins here:** SGD is a *linear* model — it can\n",
+    "only draw one straight decision boundary through 784-dimensional pixel space.\n",
+    "Random Forest is an ensemble of trees that can carve out far more complex,\n",
+    "non-linear regions, so it captures the messier patterns in handwriting better."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "979ee2e4",
+   "metadata": {},
+   "source": [
+    "## 11. Multiclass Classification — all 10 digits at once\n",
+    "\n",
+    "So far we've only asked yes/no questions (\"is it an 8?\"). Now we classify the\n",
+    "actual digit (0–9) — a **multiclass** problem.\n",
+    "\n",
+    "`SVC` (Support Vector Classifier) doesn't natively choose between 10 classes\n",
+    "directly the simple way a binary classifier does. Under the hood scikit-learn\n",
+    "uses one of two strategies automatically:\n",
+    "\n",
+    "* **OvO (One-vs-One):** train a separate binary classifier for every *pair*\n",
+    "  of classes, then let them vote. Number of classifiers needed = `n*(n-1)/2`.\n",
+    "  For 10 digits: `10*9/2 = 45` classifiers.\n",
+    "* **OvR / OvA (One-vs-Rest / One-vs-All):** train one classifier per class,\n",
+    "  each asking \"is it this class, or anything else?\" Only `n` classifiers needed\n",
+    "  (10 for digits) — cheaper, and the default strategy for most linear models.\n",
+    "\n",
+    "`SVC` defaults to OvO internally; we also explicitly wrap it in `OneVsRestClassifier`\n",
+    "below to force the OvR strategy and compare.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "63d72ae2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OvO -> n*(n-1)/2\n",
+    "# OvR / OvA -> n\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "6243ce21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kernel',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kernel&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('degree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">degree&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">gamma&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('coef0',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">coef0&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shrinking',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">shrinking&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('probability',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">probability&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cache_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cache_size&nbsp;</td>\n",
+       "            <td class=\"value\">200</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">-1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('decision_function_shape',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">decision_function_shape&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('break_ties',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">break_ties&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "SVC()"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc = SVC()\n",
+    "svc.fit(X_train[:1000], y_train[:1000])   # subset used — SVC is slow to train on full data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "620a30c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['5'], dtype=object)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc.predict([X_train[0]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "30a1864a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.75828215,  2.74956232,  6.13809559,  8.2853702 , -0.28728967,\n",
+       "         9.30119996,  0.74228825,  3.79256174,  7.20847395,  4.85762716]])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc.decision_function([X_train[0]])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2defa346",
+   "metadata": {},
+   "source": [
+    "For multiclass, `decision_function` returns one score **per class** (10\n",
+    "numbers here) instead of a single number — the predicted class is just\n",
+    "whichever score is highest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "0dba2df1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], dtype=object)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "svc.classes_\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "7ab1cf49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['5'], dtype='<U1')"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ovr_svm = OneVsRestClassifier(SVC())\n",
+    "ovr_svm.fit(X_train[:1000], y_train[:1000])\n",
+    "ovr_svm.predict([X_train[0]])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7037f0b2",
+   "metadata": {},
+   "source": [
+    "### 11.1 Multiclass Confusion Matrix\n",
+    "\n",
+    "With 10 classes, the confusion matrix becomes a 10×10 grid instead of 2×2.\n",
+    "The diagonal is correct predictions; everything off the diagonal is where\n",
+    "the model confused one digit for another.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "845adf72",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred = cross_val_predict(svc, X_train[:1000], y_train[:1000], cv=3)\n",
+    "ConfusionMatrixDisplay.from_predictions(y_train[:1000], y_pred)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5a65cd3",
+   "metadata": {},
+   "source": [
+    "Raw counts can be misleading if some digits appear more often than others.\n",
+    "Normalizing by row (`normalize=\"true\"`) converts each row into a percentage\n",
+    "of that digit's total occurrences — easier to compare error rates fairly\n",
+    "across digits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "3adad56a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ConfusionMatrixDisplay.from_predictions(\n",
+    "    y_train[:1000], y_pred, normalize=\"true\", values_format=\".0%\"\n",
+    ")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fcac31e",
+   "metadata": {},
+   "source": [
+    "We can also zero out the diagonal's visual weight by weighting only the\n",
+    "*errors*, making misclassification patterns jump out more clearly (e.g. which\n",
+    "digits get confused with which)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "a3c9e037",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weights = (y_pred != y_train[:1000])\n",
+    "ConfusionMatrixDisplay.from_predictions(\n",
+    "    y_train[:1000], y_pred, sample_weight=weights, normalize=\"true\", values_format=\".0%\"\n",
+    ")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87ace0db",
+   "metadata": {},
+   "source": [
+    "## 12. Multilabel Classification — predicting more than one yes/no at once\n",
+    "\n",
+    "Multilabel means each sample can belong to **multiple independent\n",
+    "categories simultaneously** (not mutually exclusive, unlike multiclass).\n",
+    "\n",
+    "Here we manufacture two independent binary labels per digit:\n",
+    "* Is it **greater than 5**?\n",
+    "* Is it **even**?\n",
+    "\n",
+    "A digit can be both, neither, or just one — e.g. \"8\" is both >5 and even.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "b9d3cc20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_above_5 = (y_train > '5')\n",
+    "y_train_even = (y_train.astype(int) % 2 == 0)\n",
+    "y_train_ml = np.c_[y_train_above_5, y_train_even]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "10c7ebfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False]])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier()\n",
+    "knn.fit(X_train, y_train_ml)\n",
+    "knn.predict([X_train[0]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "3b8f69b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = cross_val_predict(knn, X_train, y_train_ml, cv=3)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd3f2a16",
+   "metadata": {},
+   "source": [
+    "### F1 with `average=\"macro\"`\n",
+    "\n",
+    "With multiple labels, F1 needs to be computed per-label first, then combined\n",
+    "into one number. `average=\"macro\"` computes F1 for each label separately and\n",
+    "takes the plain average — every label counts equally regardless of how common\n",
+    "it is. (The alternative, `average=\"weighted\"`, would weight by how many\n",
+    "samples belong to each label — useful if your labels are imbalanced.)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "80e3e5b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9781811708153896"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_train_ml, y_pred, average=\"macro\")\n",
+    "# alternative: average=\"weighted\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "647f50eb",
+   "metadata": {},
+   "source": [
+    "### 12.1 ClassifierChain — modeling label dependence\n",
+    "\n",
+    "`KNeighborsClassifier` treats the two labels as independent. `ClassifierChain`\n",
+    "instead predicts labels in sequence, feeding each predicted label as an *extra\n",
+    "input feature* to the next classifier in the chain — useful when labels are\n",
+    "correlated (e.g. knowing a digit is >5 narrows down whether it's even, since\n",
+    "6 and 8 are both >5 and even).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "2c752398",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0.]])"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain_svc = ClassifierChain(SVC(), cv=3)\n",
+    "chain_svc.fit(X_train[:2000], y_train_ml[:2000])\n",
+    "chain_svc.predict([X_train[0]])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6916530f",
+   "metadata": {},
+   "source": [
+    "## 13. Multioutput Classification — predicting a whole image\n",
+    "\n",
+    "Multioutput pushes this idea further: instead of predicting a handful of\n",
+    "labels, we predict **784 pixel values at once** — effectively, the model's\n",
+    "\"label\" for one image is another entire image.\n",
+    "\n",
+    "Here we build an image-denoising task: add random noise to digit images, then\n",
+    "train KNN to map a noisy image back to its clean original.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "61f48ba3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(40)\n",
+    "image_noise = np.random.randint(0, 100, X_train.shape)\n",
+    "X_train_noisy = X_train + image_noise\n",
+    "\n",
+    "image_noise = np.random.randint(0, 100, X_test.shape)\n",
+    "X_test_noisy = X_test + image_noise\n",
+    "\n",
+    "y_train_mc = X_train   # the \"label\" for a noisy image is the original clean image\n",
+    "y_test_mc = X_test\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "070af281",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(X_train[0].reshape(28, 28), cmap=\"binary\")\n",
+    "plt.title(\"Original\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "3b39ade7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(X_train_noisy[0].reshape(28, 28), cmap=\"binary\")\n",
+    "plt.title(\"Noisy\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "0281d301",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier()\n",
+    "knn.fit(X_train_noisy, y_train_mc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "5152848f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = knn.predict([X_test_noisy[0]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "353fafc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(pred.reshape(28, 28), cmap=\"binary\")\n",
+    "plt.title(\"Denoised (predicted)\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da233043",
+   "metadata": {},
+   "source": [
+    "## 14. Summary\n",
+    "\n",
+    "| Concept | One-line takeaway |\n",
+    "|---|---|\n",
+    "| Accuracy | Misleading on imbalanced classes — always check against a Dummy baseline |\n",
+    "| Precision | Of predicted positives, how many were right — \"don't cry wolf\" |\n",
+    "| Recall | Of actual positives, how many were caught — \"don't miss any\" |\n",
+    "| F1 | Harmonic mean of precision & recall — one number, punishes imbalance between them |\n",
+    "| Decision threshold | The cutoff that turns a continuous score into yes/no — moving it trades precision for recall |\n",
+    "| PR curve | Visualizes the precision/recall tradeoff across every threshold |\n",
+    "| ROC curve / AUC | TPR vs FPR across every threshold; AUC condenses it to one number (0.5=random, 1.0=perfect) |\n",
+    "| k-fold CV | Trains/tests k times on rotating splits so every sample gets evaluated fairly, without leaking test data into training |\n",
+    "| Multiclass (OvO/OvR) | Splitting an N-class problem into many binary problems |\n",
+    "| Multilabel | Multiple independent yes/no labels per sample |\n",
+    "| Multioutput | Predicting a whole structured output (e.g. an image) rather than a label |\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/projects/notebooks/housing-price-regression/README.md b/projects/notebooks/housing-price-regression/README.md
new file mode 100644
index 0000000..1250b42
--- /dev/null
+++ b/projects/notebooks/housing-price-regression/README.md
@@ -0,0 +1,34 @@
+# California Housing Price Prediction
+
+End-to-end supervised regression pipeline predicting median house value from census-block-level data.
+
+## Overview
+
+Full pipeline from raw data to a tuned, evaluated model: EDA, stratified train/test splitting, feature engineering, a leak-free sklearn preprocessing pipeline, baseline model comparison, cross-validation, hyperparameter tuning, and a final held-out evaluation with a confidence interval.
+
+## Approach
+
+1. **EDA** — explored feature distributions, geographic price spread, and correlation with the target. `median_income` was the strongest predictor (r ≈ 0.69).
+2. **Stratified split** — training/test split stratified on binned `median_income`, so both sets represent all income bands proportionally instead of relying on a lucky random draw.
+3. **Feature engineering** — derived ratio features (`rooms_per_house`, etc.) that are more semantically meaningful per-household than the raw per-block counts.
+4. **Preprocessing pipeline** — all transforms wrapped in sklearn `Pipeline`/`ColumnTransformer` to guarantee no data leakage and identical transforms on train/test.
+5. **Model comparison** — Linear Regression vs Decision Tree baselines, evaluated with 5-fold cross-validation (not just training accuracy).
+6. **Hyperparameter tuning** — `GridSearchCV` over tree depth and an RBF-kernel feature transform.
+7. **Final evaluation** — test set touched exactly once, after all modeling decisions were locked in.
+
+## Results
+
+| Metric | Value |
+|---|---|
+| MAE | $40,600 |
+| RMSE | $61,901 |
+| R² | 0.72 |
+| 95% CI on RMSE | [$59,141 — $64,542] |
+
+## Tech stack
+
+Python, pandas, scikit-learn, matplotlib, seaborn
+
+## Notebook
+
+[`notebook.ipynb`](notebook.ipynb)
diff --git a/projects/notebooks/housing-price-regression/housing-ml-notebook.ipynb b/projects/notebooks/housing-price-regression/housing-ml-notebook.ipynb
new file mode 100644
index 0000000..9978343
--- /dev/null
+++ b/projects/notebooks/housing-price-regression/housing-ml-notebook.ipynb
@@ -0,0 +1,15920 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "672968e0-d0ba-459a-81dc-27fa86e70a55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "78a15aa0-1a54-4540-b7c8-7fd8218ed7c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"housing.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b41a3066-b257-4f6e-a823-3ddaeda970a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-122.23</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>452600.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-122.22</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>7099.0</td>\n",
+       "      <td>1106.0</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>1138.0</td>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>358500.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1467.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>496.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>352100.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1274.0</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>558.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>341300.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1627.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>342200.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20635</th>\n",
+       "      <td>-121.09</td>\n",
+       "      <td>39.48</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1665.0</td>\n",
+       "      <td>374.0</td>\n",
+       "      <td>845.0</td>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1.5603</td>\n",
+       "      <td>78100.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20636</th>\n",
+       "      <td>-121.21</td>\n",
+       "      <td>39.49</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>697.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>356.0</td>\n",
+       "      <td>114.0</td>\n",
+       "      <td>2.5568</td>\n",
+       "      <td>77100.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20637</th>\n",
+       "      <td>-121.22</td>\n",
+       "      <td>39.43</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2254.0</td>\n",
+       "      <td>485.0</td>\n",
+       "      <td>1007.0</td>\n",
+       "      <td>433.0</td>\n",
+       "      <td>1.7000</td>\n",
+       "      <td>92300.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20638</th>\n",
+       "      <td>-121.32</td>\n",
+       "      <td>39.43</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1860.0</td>\n",
+       "      <td>409.0</td>\n",
+       "      <td>741.0</td>\n",
+       "      <td>349.0</td>\n",
+       "      <td>1.8672</td>\n",
+       "      <td>84700.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20639</th>\n",
+       "      <td>-121.24</td>\n",
+       "      <td>39.37</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>2785.0</td>\n",
+       "      <td>616.0</td>\n",
+       "      <td>1387.0</td>\n",
+       "      <td>530.0</td>\n",
+       "      <td>2.3886</td>\n",
+       "      <td>89400.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20640 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "0        -122.23     37.88                41.0        880.0           129.0   \n",
+       "1        -122.22     37.86                21.0       7099.0          1106.0   \n",
+       "2        -122.24     37.85                52.0       1467.0           190.0   \n",
+       "3        -122.25     37.85                52.0       1274.0           235.0   \n",
+       "4        -122.25     37.85                52.0       1627.0           280.0   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "20635    -121.09     39.48                25.0       1665.0           374.0   \n",
+       "20636    -121.21     39.49                18.0        697.0           150.0   \n",
+       "20637    -121.22     39.43                17.0       2254.0           485.0   \n",
+       "20638    -121.32     39.43                18.0       1860.0           409.0   \n",
+       "20639    -121.24     39.37                16.0       2785.0           616.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "0           322.0       126.0         8.3252            452600.0   \n",
+       "1          2401.0      1138.0         8.3014            358500.0   \n",
+       "2           496.0       177.0         7.2574            352100.0   \n",
+       "3           558.0       219.0         5.6431            341300.0   \n",
+       "4           565.0       259.0         3.8462            342200.0   \n",
+       "...           ...         ...            ...                 ...   \n",
+       "20635       845.0       330.0         1.5603             78100.0   \n",
+       "20636       356.0       114.0         2.5568             77100.0   \n",
+       "20637      1007.0       433.0         1.7000             92300.0   \n",
+       "20638       741.0       349.0         1.8672             84700.0   \n",
+       "20639      1387.0       530.0         2.3886             89400.0   \n",
+       "\n",
+       "      ocean_proximity  \n",
+       "0            NEAR BAY  \n",
+       "1            NEAR BAY  \n",
+       "2            NEAR BAY  \n",
+       "3            NEAR BAY  \n",
+       "4            NEAR BAY  \n",
+       "...               ...  \n",
+       "20635          INLAND  \n",
+       "20636          INLAND  \n",
+       "20637          INLAND  \n",
+       "20638          INLAND  \n",
+       "20639          INLAND  \n",
+       "\n",
+       "[20640 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fa39cc9c-8e33-47e1-9dd7-c726bd694414",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 20640 entries, 0 to 20639\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           20640 non-null  float64\n",
+      " 1   latitude            20640 non-null  float64\n",
+      " 2   housing_median_age  20640 non-null  float64\n",
+      " 3   total_rooms         20640 non-null  float64\n",
+      " 4   total_bedrooms      20433 non-null  float64\n",
+      " 5   population          20640 non-null  float64\n",
+      " 6   households          20640 non-null  float64\n",
+      " 7   median_income       20640 non-null  float64\n",
+      " 8   median_house_value  20640 non-null  float64\n",
+      " 9   ocean_proximity     20640 non-null  object \n",
+      "dtypes: float64(9), object(1)\n",
+      "memory usage: 1.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "eb85a210-c746-4bc5-946e-29bee08e4719",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ocean_proximity\n",
+       "<1H OCEAN     9136\n",
+       "INLAND        6551\n",
+       "NEAR OCEAN    2658\n",
+       "NEAR BAY      2290\n",
+       "ISLAND           5\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.ocean_proximity.value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9264960e-9bb2-4a16-afb8-63375adefba6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4f891d84-2aa3-4354-911c-f2274dc473f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20433.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-119.569704</td>\n",
+       "      <td>35.631861</td>\n",
+       "      <td>28.639486</td>\n",
+       "      <td>2635.763081</td>\n",
+       "      <td>537.870553</td>\n",
+       "      <td>1425.476744</td>\n",
+       "      <td>499.539680</td>\n",
+       "      <td>3.870671</td>\n",
+       "      <td>206855.816909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.003532</td>\n",
+       "      <td>2.135952</td>\n",
+       "      <td>12.585558</td>\n",
+       "      <td>2181.615252</td>\n",
+       "      <td>421.385070</td>\n",
+       "      <td>1132.462122</td>\n",
+       "      <td>382.329753</td>\n",
+       "      <td>1.899822</td>\n",
+       "      <td>115395.615874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-124.350000</td>\n",
+       "      <td>32.540000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.499900</td>\n",
+       "      <td>14999.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-121.800000</td>\n",
+       "      <td>33.930000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>1447.750000</td>\n",
+       "      <td>296.000000</td>\n",
+       "      <td>787.000000</td>\n",
+       "      <td>280.000000</td>\n",
+       "      <td>2.563400</td>\n",
+       "      <td>119600.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>-118.490000</td>\n",
+       "      <td>34.260000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>2127.000000</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>1166.000000</td>\n",
+       "      <td>409.000000</td>\n",
+       "      <td>3.534800</td>\n",
+       "      <td>179700.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>-118.010000</td>\n",
+       "      <td>37.710000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>3148.000000</td>\n",
+       "      <td>647.000000</td>\n",
+       "      <td>1725.000000</td>\n",
+       "      <td>605.000000</td>\n",
+       "      <td>4.743250</td>\n",
+       "      <td>264725.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>-114.310000</td>\n",
+       "      <td>41.950000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>39320.000000</td>\n",
+       "      <td>6445.000000</td>\n",
+       "      <td>35682.000000</td>\n",
+       "      <td>6082.000000</td>\n",
+       "      <td>15.000100</td>\n",
+       "      <td>500001.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
+       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
+       "std        2.003532      2.135952           12.585558   2181.615252   \n",
+       "min     -124.350000     32.540000            1.000000      2.000000   \n",
+       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
+       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
+       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
+       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
+       "\n",
+       "       total_bedrooms    population    households  median_income  \\\n",
+       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
+       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
+       "std        421.385070   1132.462122    382.329753       1.899822   \n",
+       "min          1.000000      3.000000      1.000000       0.499900   \n",
+       "25%        296.000000    787.000000    280.000000       2.563400   \n",
+       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
+       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
+       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+       "\n",
+       "       median_house_value  \n",
+       "count        20640.000000  \n",
+       "mean        206855.816909  \n",
+       "std         115395.615874  \n",
+       "min          14999.000000  \n",
+       "25%         119600.000000  \n",
+       "50%         179700.000000  \n",
+       "75%         264725.000000  \n",
+       "max         500001.000000  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ed19073e-94a3-4b3f-bec5-eb9d1fc78d22",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<Axes: title={'center': 'longitude'}>,\n",
+       "        <Axes: title={'center': 'latitude'}>,\n",
+       "        <Axes: title={'center': 'housing_median_age'}>],\n",
+       "       [<Axes: title={'center': 'total_rooms'}>,\n",
+       "        <Axes: title={'center': 'total_bedrooms'}>,\n",
+       "        <Axes: title={'center': 'population'}>],\n",
+       "       [<Axes: title={'center': 'households'}>,\n",
+       "        <Axes: title={'center': 'median_income'}>,\n",
+       "        <Axes: title={'center': 'median_house_value'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x700 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(bins=40,figsize=(11,7))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39a18e13-0c74-42b4-80c4-c113899bc037",
+   "metadata": {},
+   "source": [
+    "#test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f911fdd5-cc05-4052-8d55-6a5a69c46cd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def suffle_and_split_df(df,test_ratio):\n",
+    "    np.random.seed(40)\n",
+    "    random_indices= np.random.permutation(len(df))\n",
+    "    test_set_size = int(len(df) * test_ratio)\n",
+    "    test_random_indices = random_indices[:test_set_size]\n",
+    "    train_random_indices= random_indices[test_set_size:]\n",
+    "    return df.iloc[train_random_indices],df.iloc[test_random_indices]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "fa9920e7-af7a-4484-9fc3-f620f315ad1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_set,test_set = suffle_and_split_df(df,0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "84bd123a-ffc5-44fc-93e9-c75653129ba4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9226</th>\n",
+       "      <td>-120.04</td>\n",
+       "      <td>36.93</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>3606.0</td>\n",
+       "      <td>699.0</td>\n",
+       "      <td>2074.0</td>\n",
+       "      <td>644.0</td>\n",
+       "      <td>2.6941</td>\n",
+       "      <td>63300.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11826</th>\n",
+       "      <td>-121.04</td>\n",
+       "      <td>39.00</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>4059.0</td>\n",
+       "      <td>730.0</td>\n",
+       "      <td>1874.0</td>\n",
+       "      <td>693.0</td>\n",
+       "      <td>4.8051</td>\n",
+       "      <td>174300.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4553</th>\n",
+       "      <td>-118.24</td>\n",
+       "      <td>34.06</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>199.0</td>\n",
+       "      <td>435.0</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>1.1979</td>\n",
+       "      <td>350000.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10787</th>\n",
+       "      <td>-117.92</td>\n",
+       "      <td>33.64</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>2224.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>985.0</td>\n",
+       "      <td>516.0</td>\n",
+       "      <td>3.1305</td>\n",
+       "      <td>268800.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4104</th>\n",
+       "      <td>-118.35</td>\n",
+       "      <td>34.13</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>278.0</td>\n",
+       "      <td>511.0</td>\n",
+       "      <td>278.0</td>\n",
+       "      <td>4.3333</td>\n",
+       "      <td>385900.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11532</th>\n",
+       "      <td>-118.09</td>\n",
+       "      <td>33.77</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>5359.0</td>\n",
+       "      <td>1508.0</td>\n",
+       "      <td>1829.0</td>\n",
+       "      <td>1393.0</td>\n",
+       "      <td>1.7675</td>\n",
+       "      <td>61300.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16065</th>\n",
+       "      <td>-122.48</td>\n",
+       "      <td>37.75</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>2203.0</td>\n",
+       "      <td>407.0</td>\n",
+       "      <td>1052.0</td>\n",
+       "      <td>405.0</td>\n",
+       "      <td>4.4375</td>\n",
+       "      <td>329200.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14501</th>\n",
+       "      <td>-117.23</td>\n",
+       "      <td>32.86</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>468.0</td>\n",
+       "      <td>648.0</td>\n",
+       "      <td>443.0</td>\n",
+       "      <td>3.0450</td>\n",
+       "      <td>100000.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14555</th>\n",
+       "      <td>-117.13</td>\n",
+       "      <td>32.96</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2267.0</td>\n",
+       "      <td>292.0</td>\n",
+       "      <td>1180.0</td>\n",
+       "      <td>289.0</td>\n",
+       "      <td>6.7120</td>\n",
+       "      <td>240200.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11590</th>\n",
+       "      <td>-118.01</td>\n",
+       "      <td>33.78</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2343.0</td>\n",
+       "      <td>377.0</td>\n",
+       "      <td>1166.0</td>\n",
+       "      <td>373.0</td>\n",
+       "      <td>6.0000</td>\n",
+       "      <td>233100.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16512 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "9226     -120.04     36.93                11.0       3606.0           699.0   \n",
+       "11826    -121.04     39.00                21.0       4059.0           730.0   \n",
+       "4553     -118.24     34.06                33.0        390.0           199.0   \n",
+       "10787    -117.92     33.64                25.0       2224.0           580.0   \n",
+       "4104     -118.35     34.13                39.0       1610.0           278.0   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "11532    -118.09     33.77                26.0       5359.0          1508.0   \n",
+       "16065    -122.48     37.75                49.0       2203.0           407.0   \n",
+       "14501    -117.23     32.86                16.0       1200.0           468.0   \n",
+       "14555    -117.13     32.96                15.0       2267.0           292.0   \n",
+       "11590    -118.01     33.78                26.0       2343.0           377.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "9226       2074.0       644.0         2.6941             63300.0   \n",
+       "11826      1874.0       693.0         4.8051            174300.0   \n",
+       "4553        435.0       193.0         1.1979            350000.0   \n",
+       "10787       985.0       516.0         3.1305            268800.0   \n",
+       "4104        511.0       278.0         4.3333            385900.0   \n",
+       "...           ...         ...            ...                 ...   \n",
+       "11532      1829.0      1393.0         1.7675             61300.0   \n",
+       "16065      1052.0       405.0         4.4375            329200.0   \n",
+       "14501       648.0       443.0         3.0450            100000.0   \n",
+       "14555      1180.0       289.0         6.7120            240200.0   \n",
+       "11590      1166.0       373.0         6.0000            233100.0   \n",
+       "\n",
+       "      ocean_proximity  \n",
+       "9226           INLAND  \n",
+       "11826          INLAND  \n",
+       "4553        <1H OCEAN  \n",
+       "10787       <1H OCEAN  \n",
+       "4104        <1H OCEAN  \n",
+       "...               ...  \n",
+       "11532       <1H OCEAN  \n",
+       "16065        NEAR BAY  \n",
+       "14501      NEAR OCEAN  \n",
+       "14555       <1H OCEAN  \n",
+       "11590       <1H OCEAN  \n",
+       "\n",
+       "[16512 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_set\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "06692a5e-0d15-4c5f-b4c3-04228f0f9f26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6607</th>\n",
+       "      <td>-118.14</td>\n",
+       "      <td>34.18</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>3457.0</td>\n",
+       "      <td>622.0</td>\n",
+       "      <td>1700.0</td>\n",
+       "      <td>579.0</td>\n",
+       "      <td>3.5164</td>\n",
+       "      <td>226500.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>884</th>\n",
+       "      <td>-121.97</td>\n",
+       "      <td>37.54</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>1949.0</td>\n",
+       "      <td>344.0</td>\n",
+       "      <td>986.0</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>4.6349</td>\n",
+       "      <td>196200.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9457</th>\n",
+       "      <td>-123.84</td>\n",
+       "      <td>39.83</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1461.0</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>515.0</td>\n",
+       "      <td>227.0</td>\n",
+       "      <td>1.5278</td>\n",
+       "      <td>145800.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18681</th>\n",
+       "      <td>-121.82</td>\n",
+       "      <td>36.86</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1573.0</td>\n",
+       "      <td>272.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>2.1719</td>\n",
+       "      <td>420000.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4654</th>\n",
+       "      <td>-118.33</td>\n",
+       "      <td>34.05</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>2405.0</td>\n",
+       "      <td>527.0</td>\n",
+       "      <td>1868.0</td>\n",
+       "      <td>502.0</td>\n",
+       "      <td>3.3750</td>\n",
+       "      <td>257800.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19314</th>\n",
+       "      <td>-122.87</td>\n",
+       "      <td>38.43</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>1987.0</td>\n",
+       "      <td>387.0</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>347.0</td>\n",
+       "      <td>4.0446</td>\n",
+       "      <td>172200.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15433</th>\n",
+       "      <td>-117.25</td>\n",
+       "      <td>33.20</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2050.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>1302.0</td>\n",
+       "      <td>471.0</td>\n",
+       "      <td>2.7961</td>\n",
+       "      <td>131300.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3631</th>\n",
+       "      <td>-118.43</td>\n",
+       "      <td>34.23</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2049.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>1286.0</td>\n",
+       "      <td>385.0</td>\n",
+       "      <td>4.4432</td>\n",
+       "      <td>181500.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3689</th>\n",
+       "      <td>-118.36</td>\n",
+       "      <td>34.23</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2485.0</td>\n",
+       "      <td>742.0</td>\n",
+       "      <td>1994.0</td>\n",
+       "      <td>670.0</td>\n",
+       "      <td>2.8333</td>\n",
+       "      <td>183200.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11895</th>\n",
+       "      <td>-117.36</td>\n",
+       "      <td>33.97</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>1625.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>1212.0</td>\n",
+       "      <td>327.0</td>\n",
+       "      <td>2.7596</td>\n",
+       "      <td>82200.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4128 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "6607     -118.14     34.18                47.0       3457.0           622.0   \n",
+       "884      -121.97     37.54                31.0       1949.0           344.0   \n",
+       "9457     -123.84     39.83                19.0       1461.0           340.0   \n",
+       "18681    -121.82     36.86                17.0       1573.0           272.0   \n",
+       "4654     -118.33     34.05                48.0       2405.0           527.0   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "19314    -122.87     38.43                36.0       1987.0           387.0   \n",
+       "15433    -117.25     33.20                10.0       2050.0           473.0   \n",
+       "3631     -118.43     34.23                35.0       2049.0           390.0   \n",
+       "3689     -118.36     34.23                15.0       2485.0           742.0   \n",
+       "11895    -117.36     33.97                32.0       1625.0           335.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "6607       1700.0       579.0         3.5164            226500.0   \n",
+       "884         986.0       322.0         4.6349            196200.0   \n",
+       "9457        515.0       227.0         1.5278            145800.0   \n",
+       "18681       142.0        55.0         2.1719            420000.0   \n",
+       "4654       1868.0       502.0         3.3750            257800.0   \n",
+       "...           ...         ...            ...                 ...   \n",
+       "19314      1065.0       347.0         4.0446            172200.0   \n",
+       "15433      1302.0       471.0         2.7961            131300.0   \n",
+       "3631       1286.0       385.0         4.4432            181500.0   \n",
+       "3689       1994.0       670.0         2.8333            183200.0   \n",
+       "11895      1212.0       327.0         2.7596             82200.0   \n",
+       "\n",
+       "      ocean_proximity  \n",
+       "6607        <1H OCEAN  \n",
+       "884         <1H OCEAN  \n",
+       "9457       NEAR OCEAN  \n",
+       "18681      NEAR OCEAN  \n",
+       "4654        <1H OCEAN  \n",
+       "...               ...  \n",
+       "19314       <1H OCEAN  \n",
+       "15433       <1H OCEAN  \n",
+       "3631        <1H OCEAN  \n",
+       "3689        <1H OCEAN  \n",
+       "11895          INLAND  \n",
+       "\n",
+       "[4128 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "df9d638b-b4fc-4fab-83cf-16c65846a891",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from zlib import crc32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "7e301aae-591c-4939-853d-9aa23b350b75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def is_identifier_in_test_set(identifier,test_ratio):\n",
+    "    return crc32(np.int64(identifier)) < test_ratio *2 ** 32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0a8e8e64-66fb-4ab1-8ddb-d8458c5ba6bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def  split_train_test_with_identifier_hash(df,test_ratio,identifier_column):\n",
+    "    identifiers =df[identifier_column]\n",
+    "    in_test_set = identifiers.apply(lambda id_:is_identifier_in_test_set(id_,test_ratio))\n",
+    "    return df.loc[~in_test_set],df.loc[in_test_set]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "df365973-acc7-4545-ac87-77b6c4433ddf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_set,test_set = split_train_test_with_identifier_hash(df.reset_index(),0.2,\"index\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6d235b1e-679c-4a1d-ac2a-3a0953f29885",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_with_identifier = df\n",
+    "df_with_identifier[\"identifier\"] = df[\"longitude\"]*1000+ df[\"latitude\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "dc101e16-2262-4af9-9bf5-75b63e75eca8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_set,test_set = split_train_test_with_identifier_hash(df_with_identifier,0.2,\"identifier\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "0d87f499-a430-4c1a-b958-92edf8010e14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e73b385c-5c5a-4d69-af3e-3738e5cf238f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_set,test_set = train_test_split(df,test_size=0.2,random_state=40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3502c752-f578-4bf8-a4e8-840b911ac13c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "      <th>identifier</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9226</th>\n",
+       "      <td>-120.04</td>\n",
+       "      <td>36.93</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>3606.0</td>\n",
+       "      <td>699.0</td>\n",
+       "      <td>2074.0</td>\n",
+       "      <td>644.0</td>\n",
+       "      <td>2.6941</td>\n",
+       "      <td>63300.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-120003.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11826</th>\n",
+       "      <td>-121.04</td>\n",
+       "      <td>39.00</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>4059.0</td>\n",
+       "      <td>730.0</td>\n",
+       "      <td>1874.0</td>\n",
+       "      <td>693.0</td>\n",
+       "      <td>4.8051</td>\n",
+       "      <td>174300.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121001.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4553</th>\n",
+       "      <td>-118.24</td>\n",
+       "      <td>34.06</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>199.0</td>\n",
+       "      <td>435.0</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>1.1979</td>\n",
+       "      <td>350000.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118205.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10787</th>\n",
+       "      <td>-117.92</td>\n",
+       "      <td>33.64</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>2224.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>985.0</td>\n",
+       "      <td>516.0</td>\n",
+       "      <td>3.1305</td>\n",
+       "      <td>268800.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-117886.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4104</th>\n",
+       "      <td>-118.35</td>\n",
+       "      <td>34.13</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>278.0</td>\n",
+       "      <td>511.0</td>\n",
+       "      <td>278.0</td>\n",
+       "      <td>4.3333</td>\n",
+       "      <td>385900.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118315.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11532</th>\n",
+       "      <td>-118.09</td>\n",
+       "      <td>33.77</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>5359.0</td>\n",
+       "      <td>1508.0</td>\n",
+       "      <td>1829.0</td>\n",
+       "      <td>1393.0</td>\n",
+       "      <td>1.7675</td>\n",
+       "      <td>61300.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118056.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16065</th>\n",
+       "      <td>-122.48</td>\n",
+       "      <td>37.75</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>2203.0</td>\n",
+       "      <td>407.0</td>\n",
+       "      <td>1052.0</td>\n",
+       "      <td>405.0</td>\n",
+       "      <td>4.4375</td>\n",
+       "      <td>329200.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "      <td>-122442.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14501</th>\n",
+       "      <td>-117.23</td>\n",
+       "      <td>32.86</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>468.0</td>\n",
+       "      <td>648.0</td>\n",
+       "      <td>443.0</td>\n",
+       "      <td>3.0450</td>\n",
+       "      <td>100000.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "      <td>-117197.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14555</th>\n",
+       "      <td>-117.13</td>\n",
+       "      <td>32.96</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2267.0</td>\n",
+       "      <td>292.0</td>\n",
+       "      <td>1180.0</td>\n",
+       "      <td>289.0</td>\n",
+       "      <td>6.7120</td>\n",
+       "      <td>240200.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-117097.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11590</th>\n",
+       "      <td>-118.01</td>\n",
+       "      <td>33.78</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2343.0</td>\n",
+       "      <td>377.0</td>\n",
+       "      <td>1166.0</td>\n",
+       "      <td>373.0</td>\n",
+       "      <td>6.0000</td>\n",
+       "      <td>233100.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-117976.22</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16512 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "9226     -120.04     36.93                11.0       3606.0           699.0   \n",
+       "11826    -121.04     39.00                21.0       4059.0           730.0   \n",
+       "4553     -118.24     34.06                33.0        390.0           199.0   \n",
+       "10787    -117.92     33.64                25.0       2224.0           580.0   \n",
+       "4104     -118.35     34.13                39.0       1610.0           278.0   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "11532    -118.09     33.77                26.0       5359.0          1508.0   \n",
+       "16065    -122.48     37.75                49.0       2203.0           407.0   \n",
+       "14501    -117.23     32.86                16.0       1200.0           468.0   \n",
+       "14555    -117.13     32.96                15.0       2267.0           292.0   \n",
+       "11590    -118.01     33.78                26.0       2343.0           377.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "9226       2074.0       644.0         2.6941             63300.0   \n",
+       "11826      1874.0       693.0         4.8051            174300.0   \n",
+       "4553        435.0       193.0         1.1979            350000.0   \n",
+       "10787       985.0       516.0         3.1305            268800.0   \n",
+       "4104        511.0       278.0         4.3333            385900.0   \n",
+       "...           ...         ...            ...                 ...   \n",
+       "11532      1829.0      1393.0         1.7675             61300.0   \n",
+       "16065      1052.0       405.0         4.4375            329200.0   \n",
+       "14501       648.0       443.0         3.0450            100000.0   \n",
+       "14555      1180.0       289.0         6.7120            240200.0   \n",
+       "11590      1166.0       373.0         6.0000            233100.0   \n",
+       "\n",
+       "      ocean_proximity  identifier  \n",
+       "9226           INLAND  -120003.07  \n",
+       "11826          INLAND  -121001.00  \n",
+       "4553        <1H OCEAN  -118205.94  \n",
+       "10787       <1H OCEAN  -117886.36  \n",
+       "4104        <1H OCEAN  -118315.87  \n",
+       "...               ...         ...  \n",
+       "11532       <1H OCEAN  -118056.23  \n",
+       "16065        NEAR BAY  -122442.25  \n",
+       "14501      NEAR OCEAN  -117197.14  \n",
+       "14555       <1H OCEAN  -117097.04  \n",
+       "11590       <1H OCEAN  -117976.22  \n",
+       "\n",
+       "[16512 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "510aed03-e297-4135-a628-ae36ddbebb17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "      <th>identifier</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6607</th>\n",
+       "      <td>-118.14</td>\n",
+       "      <td>34.18</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>3457.0</td>\n",
+       "      <td>622.0</td>\n",
+       "      <td>1700.0</td>\n",
+       "      <td>579.0</td>\n",
+       "      <td>3.5164</td>\n",
+       "      <td>226500.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118105.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>884</th>\n",
+       "      <td>-121.97</td>\n",
+       "      <td>37.54</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>1949.0</td>\n",
+       "      <td>344.0</td>\n",
+       "      <td>986.0</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>4.6349</td>\n",
+       "      <td>196200.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-121932.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9457</th>\n",
+       "      <td>-123.84</td>\n",
+       "      <td>39.83</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1461.0</td>\n",
+       "      <td>340.0</td>\n",
+       "      <td>515.0</td>\n",
+       "      <td>227.0</td>\n",
+       "      <td>1.5278</td>\n",
+       "      <td>145800.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "      <td>-123800.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18681</th>\n",
+       "      <td>-121.82</td>\n",
+       "      <td>36.86</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1573.0</td>\n",
+       "      <td>272.0</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>2.1719</td>\n",
+       "      <td>420000.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "      <td>-121783.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4654</th>\n",
+       "      <td>-118.33</td>\n",
+       "      <td>34.05</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>2405.0</td>\n",
+       "      <td>527.0</td>\n",
+       "      <td>1868.0</td>\n",
+       "      <td>502.0</td>\n",
+       "      <td>3.3750</td>\n",
+       "      <td>257800.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118295.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19314</th>\n",
+       "      <td>-122.87</td>\n",
+       "      <td>38.43</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>1987.0</td>\n",
+       "      <td>387.0</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>347.0</td>\n",
+       "      <td>4.0446</td>\n",
+       "      <td>172200.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-122831.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15433</th>\n",
+       "      <td>-117.25</td>\n",
+       "      <td>33.20</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2050.0</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>1302.0</td>\n",
+       "      <td>471.0</td>\n",
+       "      <td>2.7961</td>\n",
+       "      <td>131300.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-117216.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3631</th>\n",
+       "      <td>-118.43</td>\n",
+       "      <td>34.23</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2049.0</td>\n",
+       "      <td>390.0</td>\n",
+       "      <td>1286.0</td>\n",
+       "      <td>385.0</td>\n",
+       "      <td>4.4432</td>\n",
+       "      <td>181500.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118395.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3689</th>\n",
+       "      <td>-118.36</td>\n",
+       "      <td>34.23</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2485.0</td>\n",
+       "      <td>742.0</td>\n",
+       "      <td>1994.0</td>\n",
+       "      <td>670.0</td>\n",
+       "      <td>2.8333</td>\n",
+       "      <td>183200.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118325.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11895</th>\n",
+       "      <td>-117.36</td>\n",
+       "      <td>33.97</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>1625.0</td>\n",
+       "      <td>335.0</td>\n",
+       "      <td>1212.0</td>\n",
+       "      <td>327.0</td>\n",
+       "      <td>2.7596</td>\n",
+       "      <td>82200.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-117326.03</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4128 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "6607     -118.14     34.18                47.0       3457.0           622.0   \n",
+       "884      -121.97     37.54                31.0       1949.0           344.0   \n",
+       "9457     -123.84     39.83                19.0       1461.0           340.0   \n",
+       "18681    -121.82     36.86                17.0       1573.0           272.0   \n",
+       "4654     -118.33     34.05                48.0       2405.0           527.0   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "19314    -122.87     38.43                36.0       1987.0           387.0   \n",
+       "15433    -117.25     33.20                10.0       2050.0           473.0   \n",
+       "3631     -118.43     34.23                35.0       2049.0           390.0   \n",
+       "3689     -118.36     34.23                15.0       2485.0           742.0   \n",
+       "11895    -117.36     33.97                32.0       1625.0           335.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "6607       1700.0       579.0         3.5164            226500.0   \n",
+       "884         986.0       322.0         4.6349            196200.0   \n",
+       "9457        515.0       227.0         1.5278            145800.0   \n",
+       "18681       142.0        55.0         2.1719            420000.0   \n",
+       "4654       1868.0       502.0         3.3750            257800.0   \n",
+       "...           ...         ...            ...                 ...   \n",
+       "19314      1065.0       347.0         4.0446            172200.0   \n",
+       "15433      1302.0       471.0         2.7961            131300.0   \n",
+       "3631       1286.0       385.0         4.4432            181500.0   \n",
+       "3689       1994.0       670.0         2.8333            183200.0   \n",
+       "11895      1212.0       327.0         2.7596             82200.0   \n",
+       "\n",
+       "      ocean_proximity  identifier  \n",
+       "6607        <1H OCEAN  -118105.82  \n",
+       "884         <1H OCEAN  -121932.46  \n",
+       "9457       NEAR OCEAN  -123800.17  \n",
+       "18681      NEAR OCEAN  -121783.14  \n",
+       "4654        <1H OCEAN  -118295.95  \n",
+       "...               ...         ...  \n",
+       "19314       <1H OCEAN  -122831.57  \n",
+       "15433       <1H OCEAN  -117216.80  \n",
+       "3631        <1H OCEAN  -118395.77  \n",
+       "3689        <1H OCEAN  -118325.77  \n",
+       "11895          INLAND  -117326.03  \n",
+       "\n",
+       "[4128 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "9a1b0509-62ad-419f-aa73-b5e6a20445da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"median_income_categories\"] = pd.cut(df[\"median_income\"],bins =[0.0,1.5,3.0,4.5,6.0,np.inf],\n",
+    "      labels = [1,2,3,4,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "cf1b3843-9a49-40b9-98aa-0e0295240b3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='median_income_categories'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"median_income_categories\"].value_counts().sort_index().plot.bar(grid=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a56f7025-7ebe-412c-81c9-d7214aa86baf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import StratifiedShuffleSplit\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4a681c3a-712f-4928-b4a8-c2936a3c7cb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sss = StratifiedShuffleSplit(n_splits=10,test_size=0.2,random_state=40)\n",
+    "stratified_split = []\n",
+    "for train_i,test_i in sss.split(df,df[\"median_income_categories\"]):\n",
+    "    stratified_train_set_n =  df.iloc[train_i]\n",
+    "    stratified_test_set_n =  df.iloc[test_i]\n",
+    "    stratified_split.append([stratified_train_set_n,stratified_test_set_n])\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d488995a-cd18-4559-b2e7-6d5f2e1578ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(stratified_split)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "442596f7-a739-4394-8795-35591aa41e03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stratified_train_set,stratified_test_set = stratified_split[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2ce6dc8f-1c91-4b95-b941-c85f3d0400a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
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+       "      <th>housing_median_age</th>\n",
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+       "      <th>median_income_categories</th>\n",
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+       "      <th>16415</th>\n",
+       "      <td>-121.26</td>\n",
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+       "      <td>-118.15</td>\n",
+       "      <td>33.95</td>\n",
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+       "      <td>2753.0</td>\n",
+       "      <td>702.0</td>\n",
+       "      <td>1592.0</td>\n",
+       "      <td>614.0</td>\n",
+       "      <td>2.7875</td>\n",
+       "      <td>209000.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118116.05</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4425</th>\n",
+       "      <td>-118.24</td>\n",
+       "      <td>34.07</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>223.0</td>\n",
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+       "      <td>1.6136</td>\n",
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+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118205.93</td>\n",
+       "      <td>2</td>\n",
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+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>-121.82</td>\n",
+       "      <td>38.02</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>2.2361</td>\n",
+       "      <td>93800.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121781.98</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "    <tr>\n",
+       "      <th>1221</th>\n",
+       "      <td>-120.65</td>\n",
+       "      <td>38.28</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>3095.0</td>\n",
+       "      <td>681.0</td>\n",
+       "      <td>1341.0</td>\n",
+       "      <td>546.0</td>\n",
+       "      <td>2.1382</td>\n",
+       "      <td>104000.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-120611.72</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12640</th>\n",
+       "      <td>-121.45</td>\n",
+       "      <td>38.53</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>1893.0</td>\n",
+       "      <td>415.0</td>\n",
+       "      <td>884.0</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>2.1679</td>\n",
+       "      <td>75400.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121411.47</td>\n",
+       "      <td>2</td>\n",
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+       "    <tr>\n",
+       "      <th>5747</th>\n",
+       "      <td>-118.27</td>\n",
+       "      <td>34.17</td>\n",
+       "      <td>48.0</td>\n",
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+       "      <td>825.0</td>\n",
+       "      <td>269.0</td>\n",
+       "      <td>5.5118</td>\n",
+       "      <td>354700.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118235.83</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16224</th>\n",
+       "      <td>-121.33</td>\n",
+       "      <td>37.98</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>3113.0</td>\n",
+       "      <td>576.0</td>\n",
+       "      <td>1746.0</td>\n",
+       "      <td>544.0</td>\n",
+       "      <td>3.4625</td>\n",
+       "      <td>84600.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121292.02</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8967</th>\n",
+       "      <td>-118.40</td>\n",
+       "      <td>34.00</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>1534.0</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>751.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>5.4440</td>\n",
+       "      <td>336000.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118366.00</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16512 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "16415    -121.26     37.88                42.0        465.0            93.0   \n",
+       "1279     -121.64     37.85                22.0       1999.0           415.0   \n",
+       "7741     -118.15     33.95                35.0       2753.0           702.0   \n",
+       "4425     -118.24     34.07                27.0        223.0            80.0   \n",
+       "1283     -121.82     38.02                46.0        176.0            43.0   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "1221     -120.65     38.28                21.0       3095.0           681.0   \n",
+       "12640    -121.45     38.53                34.0       1893.0           415.0   \n",
+       "5747     -118.27     34.17                48.0       1560.0           280.0   \n",
+       "16224    -121.33     37.98                36.0       3113.0           576.0   \n",
+       "8967     -118.40     34.00                37.0       1534.0           258.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "16415       256.0        93.0         3.1719            158300.0   \n",
+       "1279        967.0       320.0         4.4583            253900.0   \n",
+       "7741       1592.0       614.0         2.7875            209000.0   \n",
+       "4425        249.0        82.0         1.6136            137500.0   \n",
+       "1283        101.0        40.0         2.2361             93800.0   \n",
+       "...           ...         ...            ...                 ...   \n",
+       "1221       1341.0       546.0         2.1382            104000.0   \n",
+       "12640       884.0       395.0         2.1679             75400.0   \n",
+       "5747        825.0       269.0         5.5118            354700.0   \n",
+       "16224      1746.0       544.0         3.4625             84600.0   \n",
+       "8967        751.0       259.0         5.4440            336000.0   \n",
+       "\n",
+       "      ocean_proximity  identifier median_income_categories  \n",
+       "16415          INLAND  -121222.12                        3  \n",
+       "1279           INLAND  -121602.15                        3  \n",
+       "7741        <1H OCEAN  -118116.05                        2  \n",
+       "4425        <1H OCEAN  -118205.93                        2  \n",
+       "1283           INLAND  -121781.98                        2  \n",
+       "...               ...         ...                      ...  \n",
+       "1221           INLAND  -120611.72                        2  \n",
+       "12640          INLAND  -121411.47                        2  \n",
+       "5747        <1H OCEAN  -118235.83                        4  \n",
+       "16224          INLAND  -121292.02                        3  \n",
+       "8967        <1H OCEAN  -118366.00                        4  \n",
+       "\n",
+       "[16512 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stratified_train_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "67d629f8-6121-43b0-a61b-0fc0f414b2ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
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+       "      <th>5905</th>\n",
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+       "      <td>3</td>\n",
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+       "    <tr>\n",
+       "      <th>4830</th>\n",
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+       "      <td>4943.0</td>\n",
+       "      <td>851.0</td>\n",
+       "      <td>2164.0</td>\n",
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+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-117836.24</td>\n",
+       "      <td>3</td>\n",
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+       "    <tr>\n",
+       "      <th>7048</th>\n",
+       "      <td>-118.05</td>\n",
+       "      <td>33.94</td>\n",
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+       "      <td>1.9453</td>\n",
+       "      <td>149000.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118016.06</td>\n",
+       "      <td>2</td>\n",
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+       "    <tr>\n",
+       "      <th>13721</th>\n",
+       "      <td>-117.18</td>\n",
+       "      <td>34.07</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1347.0</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>799.0</td>\n",
+       "      <td>276.0</td>\n",
+       "      <td>2.9485</td>\n",
+       "      <td>112500.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-117145.93</td>\n",
+       "      <td>2</td>\n",
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+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>3377</th>\n",
+       "      <td>-118.29</td>\n",
+       "      <td>34.25</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>1988.0</td>\n",
+       "      <td>594.0</td>\n",
+       "      <td>1399.0</td>\n",
+       "      <td>527.0</td>\n",
+       "      <td>2.4727</td>\n",
+       "      <td>175000.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118255.75</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19816</th>\n",
+       "      <td>-119.29</td>\n",
+       "      <td>36.54</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2581.0</td>\n",
+       "      <td>628.0</td>\n",
+       "      <td>2732.0</td>\n",
+       "      <td>592.0</td>\n",
+       "      <td>1.8429</td>\n",
+       "      <td>58300.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-119253.46</td>\n",
+       "      <td>2</td>\n",
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+       "    <tr>\n",
+       "      <th>20545</th>\n",
+       "      <td>-121.75</td>\n",
+       "      <td>38.55</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>4802.0</td>\n",
+       "      <td>950.0</td>\n",
+       "      <td>2199.0</td>\n",
+       "      <td>939.0</td>\n",
+       "      <td>3.7452</td>\n",
+       "      <td>227700.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121711.45</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3330</th>\n",
+       "      <td>-122.48</td>\n",
+       "      <td>38.90</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>161.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>2.1964</td>\n",
+       "      <td>112500.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-122441.10</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14222</th>\n",
+       "      <td>-117.04</td>\n",
+       "      <td>32.66</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>3362.0</td>\n",
+       "      <td>630.0</td>\n",
+       "      <td>1471.0</td>\n",
+       "      <td>612.0</td>\n",
+       "      <td>4.1442</td>\n",
+       "      <td>303900.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "      <td>-117007.34</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4128 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "5905     -118.42     34.29                34.0       1489.0           326.0   \n",
+       "4830     -118.30     34.04                37.0       1470.0           399.0   \n",
+       "10969    -117.87     33.76                37.0       4943.0           851.0   \n",
+       "7048     -118.05     33.94                34.0        495.0           120.0   \n",
+       "13721    -117.18     34.07                 7.0       1347.0           301.0   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "3377     -118.29     34.25                19.0       1988.0           594.0   \n",
+       "19816    -119.29     36.54                18.0       2581.0           628.0   \n",
+       "20545    -121.75     38.55                26.0       4802.0           950.0   \n",
+       "3330     -122.48     38.90                10.0        304.0            63.0   \n",
+       "14222    -117.04     32.66                22.0       3362.0           630.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "5905       1389.0       313.0         3.4821            160300.0   \n",
+       "4830       1579.0       390.0         2.0060            150000.0   \n",
+       "10969      2164.0       788.0         4.1071            311300.0   \n",
+       "7048        527.0       130.0         1.9453            149000.0   \n",
+       "13721       799.0       276.0         2.9485            112500.0   \n",
+       "...           ...         ...            ...                 ...   \n",
+       "3377       1399.0       527.0         2.4727            175000.0   \n",
+       "19816      2732.0       592.0         1.8429             58300.0   \n",
+       "20545      2199.0       939.0         3.7452            227700.0   \n",
+       "3330        161.0        61.0         2.1964            112500.0   \n",
+       "14222      1471.0       612.0         4.1442            303900.0   \n",
+       "\n",
+       "      ocean_proximity  identifier median_income_categories  \n",
+       "5905        <1H OCEAN  -118385.71                        3  \n",
+       "4830        <1H OCEAN  -118265.96                        2  \n",
+       "10969       <1H OCEAN  -117836.24                        3  \n",
+       "7048        <1H OCEAN  -118016.06                        2  \n",
+       "13721          INLAND  -117145.93                        2  \n",
+       "...               ...         ...                      ...  \n",
+       "3377        <1H OCEAN  -118255.75                        2  \n",
+       "19816          INLAND  -119253.46                        2  \n",
+       "20545          INLAND  -121711.45                        3  \n",
+       "3330           INLAND  -122441.10                        2  \n",
+       "14222      NEAR OCEAN  -117007.34                        3  \n",
+       "\n",
+       "[4128 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stratified_test_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "d63bab1d-4554-44c6-95f7-fe01f4a1fc50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "median_income_categories\n",
+       "3    0.350533\n",
+       "2    0.318798\n",
+       "4    0.176357\n",
+       "5    0.114341\n",
+       "1    0.039971\n",
+       "Name: count, dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stratified_test_set[\"median_income_categories\"].value_counts()/len(stratified_test_set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "2011a1a5-c2d0-4ea6-8852-366ff1159b3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "median_income_categories\n",
+       "3    0.350594\n",
+       "2    0.318859\n",
+       "4    0.176296\n",
+       "5    0.114462\n",
+       "1    0.039789\n",
+       "Name: count, dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stratified_train_set[\"median_income_categories\"].value_counts()/len(stratified_train_set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "7c47231e-f9ac-40b3-b424-6c636c74448f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "str_train_set,str_test_set = train_test_split(df,test_size=0.2,stratify=df[\"median_income_categories\"],random_state=40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "eda0858d-2e66-4a5e-b229-8045f92eaf76",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "      <th>identifier</th>\n",
+       "      <th>median_income_categories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16415</th>\n",
+       "      <td>-121.26</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>465.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>3.1719</td>\n",
+       "      <td>158300.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121222.12</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>-121.64</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1999.0</td>\n",
+       "      <td>415.0</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>4.4583</td>\n",
+       "      <td>253900.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121602.15</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7741</th>\n",
+       "      <td>-118.15</td>\n",
+       "      <td>33.95</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2753.0</td>\n",
+       "      <td>702.0</td>\n",
+       "      <td>1592.0</td>\n",
+       "      <td>614.0</td>\n",
+       "      <td>2.7875</td>\n",
+       "      <td>209000.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118116.05</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4425</th>\n",
+       "      <td>-118.24</td>\n",
+       "      <td>34.07</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>223.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>249.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>1.6136</td>\n",
+       "      <td>137500.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118205.93</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>-121.82</td>\n",
+       "      <td>38.02</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>2.2361</td>\n",
+       "      <td>93800.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121781.98</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1221</th>\n",
+       "      <td>-120.65</td>\n",
+       "      <td>38.28</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>3095.0</td>\n",
+       "      <td>681.0</td>\n",
+       "      <td>1341.0</td>\n",
+       "      <td>546.0</td>\n",
+       "      <td>2.1382</td>\n",
+       "      <td>104000.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-120611.72</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12640</th>\n",
+       "      <td>-121.45</td>\n",
+       "      <td>38.53</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>1893.0</td>\n",
+       "      <td>415.0</td>\n",
+       "      <td>884.0</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>2.1679</td>\n",
+       "      <td>75400.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121411.47</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5747</th>\n",
+       "      <td>-118.27</td>\n",
+       "      <td>34.17</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>1560.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>269.0</td>\n",
+       "      <td>5.5118</td>\n",
+       "      <td>354700.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118235.83</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16224</th>\n",
+       "      <td>-121.33</td>\n",
+       "      <td>37.98</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>3113.0</td>\n",
+       "      <td>576.0</td>\n",
+       "      <td>1746.0</td>\n",
+       "      <td>544.0</td>\n",
+       "      <td>3.4625</td>\n",
+       "      <td>84600.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121292.02</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8967</th>\n",
+       "      <td>-118.40</td>\n",
+       "      <td>34.00</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>1534.0</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>751.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>5.4440</td>\n",
+       "      <td>336000.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118366.00</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16512 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "16415    -121.26     37.88                42.0        465.0            93.0   \n",
+       "1279     -121.64     37.85                22.0       1999.0           415.0   \n",
+       "7741     -118.15     33.95                35.0       2753.0           702.0   \n",
+       "4425     -118.24     34.07                27.0        223.0            80.0   \n",
+       "1283     -121.82     38.02                46.0        176.0            43.0   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "1221     -120.65     38.28                21.0       3095.0           681.0   \n",
+       "12640    -121.45     38.53                34.0       1893.0           415.0   \n",
+       "5747     -118.27     34.17                48.0       1560.0           280.0   \n",
+       "16224    -121.33     37.98                36.0       3113.0           576.0   \n",
+       "8967     -118.40     34.00                37.0       1534.0           258.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "16415       256.0        93.0         3.1719            158300.0   \n",
+       "1279        967.0       320.0         4.4583            253900.0   \n",
+       "7741       1592.0       614.0         2.7875            209000.0   \n",
+       "4425        249.0        82.0         1.6136            137500.0   \n",
+       "1283        101.0        40.0         2.2361             93800.0   \n",
+       "...           ...         ...            ...                 ...   \n",
+       "1221       1341.0       546.0         2.1382            104000.0   \n",
+       "12640       884.0       395.0         2.1679             75400.0   \n",
+       "5747        825.0       269.0         5.5118            354700.0   \n",
+       "16224      1746.0       544.0         3.4625             84600.0   \n",
+       "8967        751.0       259.0         5.4440            336000.0   \n",
+       "\n",
+       "      ocean_proximity  identifier median_income_categories  \n",
+       "16415          INLAND  -121222.12                        3  \n",
+       "1279           INLAND  -121602.15                        3  \n",
+       "7741        <1H OCEAN  -118116.05                        2  \n",
+       "4425        <1H OCEAN  -118205.93                        2  \n",
+       "1283           INLAND  -121781.98                        2  \n",
+       "...               ...         ...                      ...  \n",
+       "1221           INLAND  -120611.72                        2  \n",
+       "12640          INLAND  -121411.47                        2  \n",
+       "5747        <1H OCEAN  -118235.83                        4  \n",
+       "16224          INLAND  -121292.02                        3  \n",
+       "8967        <1H OCEAN  -118366.00                        4  \n",
+       "\n",
+       "[16512 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str_train_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "0daf16c2-8c9d-48ab-976b-7671897e9335",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "str_train_set =str_train_set.drop(\"median_income_categories\",axis=1)\n",
+    "str_test_set=str_test_set.drop(\"median_income_categories\",axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "0c6802d3-6558-4315-ab6b-4c6eecbb1afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#visualize\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "61b7f67d-9eef-4cdd-a8ed-a91f1046afd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train  = str_train_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "ccd4c936-96b4-4561-8806-0ee7111086f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.scatterplot(data=train,x=\"longitude\",y=\"latitude\",size=\"population\",alpha=0.2,hue=\"median_house_value\")\n",
+    "sns.move_legend(ax,\"upper left\",bbox_to_anchor=(1,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "0c8add65-fd8b-4fe8-bc76-d8aa573421ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "longitude            -0.051167\n",
+       "latitude             -0.137691\n",
+       "housing_median_age    0.105329\n",
+       "total_rooms           0.138007\n",
+       "total_bedrooms        0.052814\n",
+       "population           -0.021544\n",
+       "households            0.070014\n",
+       "median_income         0.686865\n",
+       "median_house_value    1.000000\n",
+       "identifier           -0.051364\n",
+       "Name: median_house_value, dtype: float64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.corr(numeric_only=True)[\"median_house_value\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "2e09207d-464b-412c-8540-8addf0193055",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='median_income', ylabel='median_house_value'>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(data=train,x=\"median_income\",y=\"median_house_value\",alpha=0.2,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "845e7288-4ac1-4ea2-a916-78e23fd37e62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# attribute combinations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "77e367b1-d89b-41f4-9122-c8b481f4166c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "      <th>identifier</th>\n",
+       "      <th>rooms_per_house</th>\n",
+       "      <th>bedrooms_ratio</th>\n",
+       "      <th>people_per_house</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16415</th>\n",
+       "      <td>-121.26</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>465.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>3.1719</td>\n",
+       "      <td>158300.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121222.12</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>2.752688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>-121.64</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1999.0</td>\n",
+       "      <td>415.0</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>4.4583</td>\n",
+       "      <td>253900.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121602.15</td>\n",
+       "      <td>6.246875</td>\n",
+       "      <td>0.207604</td>\n",
+       "      <td>3.021875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7741</th>\n",
+       "      <td>-118.15</td>\n",
+       "      <td>33.95</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2753.0</td>\n",
+       "      <td>702.0</td>\n",
+       "      <td>1592.0</td>\n",
+       "      <td>614.0</td>\n",
+       "      <td>2.7875</td>\n",
+       "      <td>209000.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118116.05</td>\n",
+       "      <td>4.483713</td>\n",
+       "      <td>0.254995</td>\n",
+       "      <td>2.592834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4425</th>\n",
+       "      <td>-118.24</td>\n",
+       "      <td>34.07</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>223.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>249.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>1.6136</td>\n",
+       "      <td>137500.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118205.93</td>\n",
+       "      <td>2.719512</td>\n",
+       "      <td>0.358744</td>\n",
+       "      <td>3.036585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>-121.82</td>\n",
+       "      <td>38.02</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>2.2361</td>\n",
+       "      <td>93800.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121781.98</td>\n",
+       "      <td>4.400000</td>\n",
+       "      <td>0.244318</td>\n",
+       "      <td>2.525000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1221</th>\n",
+       "      <td>-120.65</td>\n",
+       "      <td>38.28</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>3095.0</td>\n",
+       "      <td>681.0</td>\n",
+       "      <td>1341.0</td>\n",
+       "      <td>546.0</td>\n",
+       "      <td>2.1382</td>\n",
+       "      <td>104000.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-120611.72</td>\n",
+       "      <td>5.668498</td>\n",
+       "      <td>0.220032</td>\n",
+       "      <td>2.456044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12640</th>\n",
+       "      <td>-121.45</td>\n",
+       "      <td>38.53</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>1893.0</td>\n",
+       "      <td>415.0</td>\n",
+       "      <td>884.0</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>2.1679</td>\n",
+       "      <td>75400.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121411.47</td>\n",
+       "      <td>4.792405</td>\n",
+       "      <td>0.219229</td>\n",
+       "      <td>2.237975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5747</th>\n",
+       "      <td>-118.27</td>\n",
+       "      <td>34.17</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>1560.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>269.0</td>\n",
+       "      <td>5.5118</td>\n",
+       "      <td>354700.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118235.83</td>\n",
+       "      <td>5.799257</td>\n",
+       "      <td>0.179487</td>\n",
+       "      <td>3.066914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16224</th>\n",
+       "      <td>-121.33</td>\n",
+       "      <td>37.98</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>3113.0</td>\n",
+       "      <td>576.0</td>\n",
+       "      <td>1746.0</td>\n",
+       "      <td>544.0</td>\n",
+       "      <td>3.4625</td>\n",
+       "      <td>84600.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>-121292.02</td>\n",
+       "      <td>5.722426</td>\n",
+       "      <td>0.185031</td>\n",
+       "      <td>3.209559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8967</th>\n",
+       "      <td>-118.40</td>\n",
+       "      <td>34.00</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>1534.0</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>751.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>5.4440</td>\n",
+       "      <td>336000.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>-118366.00</td>\n",
+       "      <td>5.922780</td>\n",
+       "      <td>0.168188</td>\n",
+       "      <td>2.899614</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16512 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "16415    -121.26     37.88                42.0        465.0            93.0   \n",
+       "1279     -121.64     37.85                22.0       1999.0           415.0   \n",
+       "7741     -118.15     33.95                35.0       2753.0           702.0   \n",
+       "4425     -118.24     34.07                27.0        223.0            80.0   \n",
+       "1283     -121.82     38.02                46.0        176.0            43.0   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "1221     -120.65     38.28                21.0       3095.0           681.0   \n",
+       "12640    -121.45     38.53                34.0       1893.0           415.0   \n",
+       "5747     -118.27     34.17                48.0       1560.0           280.0   \n",
+       "16224    -121.33     37.98                36.0       3113.0           576.0   \n",
+       "8967     -118.40     34.00                37.0       1534.0           258.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "16415       256.0        93.0         3.1719            158300.0   \n",
+       "1279        967.0       320.0         4.4583            253900.0   \n",
+       "7741       1592.0       614.0         2.7875            209000.0   \n",
+       "4425        249.0        82.0         1.6136            137500.0   \n",
+       "1283        101.0        40.0         2.2361             93800.0   \n",
+       "...           ...         ...            ...                 ...   \n",
+       "1221       1341.0       546.0         2.1382            104000.0   \n",
+       "12640       884.0       395.0         2.1679             75400.0   \n",
+       "5747        825.0       269.0         5.5118            354700.0   \n",
+       "16224      1746.0       544.0         3.4625             84600.0   \n",
+       "8967        751.0       259.0         5.4440            336000.0   \n",
+       "\n",
+       "      ocean_proximity  identifier  rooms_per_house  bedrooms_ratio  \\\n",
+       "16415          INLAND  -121222.12         5.000000        0.200000   \n",
+       "1279           INLAND  -121602.15         6.246875        0.207604   \n",
+       "7741        <1H OCEAN  -118116.05         4.483713        0.254995   \n",
+       "4425        <1H OCEAN  -118205.93         2.719512        0.358744   \n",
+       "1283           INLAND  -121781.98         4.400000        0.244318   \n",
+       "...               ...         ...              ...             ...   \n",
+       "1221           INLAND  -120611.72         5.668498        0.220032   \n",
+       "12640          INLAND  -121411.47         4.792405        0.219229   \n",
+       "5747        <1H OCEAN  -118235.83         5.799257        0.179487   \n",
+       "16224          INLAND  -121292.02         5.722426        0.185031   \n",
+       "8967        <1H OCEAN  -118366.00         5.922780        0.168188   \n",
+       "\n",
+       "       people_per_house  \n",
+       "16415          2.752688  \n",
+       "1279           3.021875  \n",
+       "7741           2.592834  \n",
+       "4425           3.036585  \n",
+       "1283           2.525000  \n",
+       "...                 ...  \n",
+       "1221           2.456044  \n",
+       "12640          2.237975  \n",
+       "5747           3.066914  \n",
+       "16224          3.209559  \n",
+       "8967           2.899614  \n",
+       "\n",
+       "[16512 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"rooms_per_house\"] = train[\"total_rooms\"]/train[\"households\"]\n",
+    "train[\"bedrooms_ratio\"] =train[\"total_bedrooms\"]/train[\"total_rooms\"]\n",
+    "train[\"people_per_house\"] = train[\"population\"]/train[\"households\"]\n",
+    "train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "8ce5f436-fea5-457c-a8fa-a945dba8c61e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "longitude            -0.051167\n",
+       "latitude             -0.137691\n",
+       "housing_median_age    0.105329\n",
+       "total_rooms           0.138007\n",
+       "total_bedrooms        0.052814\n",
+       "population           -0.021544\n",
+       "households            0.070014\n",
+       "median_income         0.686865\n",
+       "median_house_value    1.000000\n",
+       "identifier           -0.051364\n",
+       "rooms_per_house       0.159500\n",
+       "bedrooms_ratio       -0.255178\n",
+       "people_per_house     -0.027110\n",
+       "Name: median_house_value, dtype: float64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.corr(numeric_only=True)[\"median_house_value\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "f80d8843-0cf3-4b27-9044-5dda62998b8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#prepare data \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "ef08898f-f031-4c46-9d00-073f15145e32",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16415    158300.0\n",
+       "1279     253900.0\n",
+       "7741     209000.0\n",
+       "4425     137500.0\n",
+       "1283      93800.0\n",
+       "           ...   \n",
+       "1221     104000.0\n",
+       "12640     75400.0\n",
+       "5747     354700.0\n",
+       "16224     84600.0\n",
+       "8967     336000.0\n",
+       "Name: median_house_value, Length: 16512, dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_features=train.drop(\"median_house_value\",axis=1 ,)\n",
+    "train_target = train[\"median_house_value\"]\n",
+    "train_target"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "0107439c-3847-48e6-afd5-4ce0318ef760",
+   "metadata": {},
+   "source": [
+    "##data cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "87f82346-c612-45e2-9010-644ef19e3d73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 16512 entries, 16415 to 8967\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           16512 non-null  float64\n",
+      " 1   latitude            16512 non-null  float64\n",
+      " 2   housing_median_age  16512 non-null  float64\n",
+      " 3   total_rooms         16512 non-null  float64\n",
+      " 4   total_bedrooms      16344 non-null  float64\n",
+      " 5   population          16512 non-null  float64\n",
+      " 6   households          16512 non-null  float64\n",
+      " 7   median_income       16512 non-null  float64\n",
+      " 8   ocean_proximity     16512 non-null  object \n",
+      " 9   identifier          16512 non-null  float64\n",
+      " 10  rooms_per_house     16512 non-null  float64\n",
+      " 11  bedrooms_ratio      16344 non-null  float64\n",
+      " 12  people_per_house    16512 non-null  float64\n",
+      "dtypes: float64(12), object(1)\n",
+      "memory usage: 1.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_features.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "9ece43d8-5c4c-42f9-999c-e695edafb662",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 16344 entries, 16415 to 8967\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           16344 non-null  float64\n",
+      " 1   latitude            16344 non-null  float64\n",
+      " 2   housing_median_age  16344 non-null  float64\n",
+      " 3   total_rooms         16344 non-null  float64\n",
+      " 4   total_bedrooms      16344 non-null  float64\n",
+      " 5   population          16344 non-null  float64\n",
+      " 6   households          16344 non-null  float64\n",
+      " 7   median_income       16344 non-null  float64\n",
+      " 8   ocean_proximity     16344 non-null  object \n",
+      " 9   identifier          16344 non-null  float64\n",
+      " 10  rooms_per_house     16344 non-null  float64\n",
+      " 11  bedrooms_ratio      16344 non-null  float64\n",
+      " 12  people_per_house    16344 non-null  float64\n",
+      "dtypes: float64(12), object(1)\n",
+      "memory usage: 1.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#remove na rows values\n",
+    "train_features.dropna(subset=[\"total_bedrooms\"]).info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "21f9bb0e-3a8b-4164-b478-efecea604e4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 16512 entries, 16415 to 8967\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           16512 non-null  float64\n",
+      " 1   latitude            16512 non-null  float64\n",
+      " 2   housing_median_age  16512 non-null  float64\n",
+      " 3   total_rooms         16512 non-null  float64\n",
+      " 4   population          16512 non-null  float64\n",
+      " 5   households          16512 non-null  float64\n",
+      " 6   median_income       16512 non-null  float64\n",
+      " 7   ocean_proximity     16512 non-null  object \n",
+      " 8   identifier          16512 non-null  float64\n",
+      " 9   rooms_per_house     16512 non-null  float64\n",
+      " 10  bedrooms_ratio      16344 non-null  float64\n",
+      " 11  people_per_house    16512 non-null  float64\n",
+      "dtypes: float64(11), object(1)\n",
+      "memory usage: 1.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# reomove the na column\n",
+    "train_features.drop([\"total_bedrooms\"],axis=1).info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "5198f677-d321-4e29-8ead-8c9713f1a1ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#imputed na values\n",
+    "total_bedrooms_median= train_features[\"total_bedrooms\"].median()\n",
+    "train_features[\"total_bedrooms\"] =train_features[\"total_bedrooms\"].fillna(total_bedrooms_median)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "c09755d9-3fd8-4b66-a8f3-2786110c1e24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 16512 entries, 16415 to 8967\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           16512 non-null  float64\n",
+      " 1   latitude            16512 non-null  float64\n",
+      " 2   housing_median_age  16512 non-null  float64\n",
+      " 3   total_rooms         16512 non-null  float64\n",
+      " 4   total_bedrooms      16512 non-null  float64\n",
+      " 5   population          16512 non-null  float64\n",
+      " 6   households          16512 non-null  float64\n",
+      " 7   median_income       16512 non-null  float64\n",
+      " 8   ocean_proximity     16512 non-null  object \n",
+      " 9   identifier          16512 non-null  float64\n",
+      " 10  rooms_per_house     16512 non-null  float64\n",
+      " 11  bedrooms_ratio      16344 non-null  float64\n",
+      " 12  people_per_house    16512 non-null  float64\n",
+      "dtypes: float64(12), object(1)\n",
+      "memory usage: 1.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_features.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "5cbafb0f-f251-4b48-8e4a-313bdbdb119e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_features[\"bedrooms_ratio\"] =train_features[\"total_bedrooms\"]/train_features[\"total_rooms\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "b258118c-fa8c-483d-a126-71b740d5a27a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 16512 entries, 16415 to 8967\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           16512 non-null  float64\n",
+      " 1   latitude            16512 non-null  float64\n",
+      " 2   housing_median_age  16512 non-null  float64\n",
+      " 3   total_rooms         16512 non-null  float64\n",
+      " 4   total_bedrooms      16512 non-null  float64\n",
+      " 5   population          16512 non-null  float64\n",
+      " 6   households          16512 non-null  float64\n",
+      " 7   median_income       16512 non-null  float64\n",
+      " 8   ocean_proximity     16512 non-null  object \n",
+      " 9   identifier          16512 non-null  float64\n",
+      " 10  rooms_per_house     16512 non-null  float64\n",
+      " 11  bedrooms_ratio      16512 non-null  float64\n",
+      " 12  people_per_house    16512 non-null  float64\n",
+      "dtypes: float64(12), object(1)\n",
+      "memory usage: 1.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_features.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "9a0d9072-56fb-4cc2-9426-50b04db3f348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sklearn impute\n",
+    "\n",
+    "from sklearn.impute import SimpleImputer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "c853730b-e3ee-49c1-80a2-6ac1a38d6a60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_imputer=SimpleImputer(strategy=\"median\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "674c93e7-c449-4fcd-94cb-2473e3d8692d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 16512 entries, 16415 to 8967\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           16512 non-null  float64\n",
+      " 1   latitude            16512 non-null  float64\n",
+      " 2   housing_median_age  16512 non-null  float64\n",
+      " 3   total_rooms         16512 non-null  float64\n",
+      " 4   total_bedrooms      16512 non-null  float64\n",
+      " 5   population          16512 non-null  float64\n",
+      " 6   households          16512 non-null  float64\n",
+      " 7   median_income       16512 non-null  float64\n",
+      " 8   ocean_proximity     16512 non-null  object \n",
+      " 9   identifier          16512 non-null  float64\n",
+      " 10  rooms_per_house     16512 non-null  float64\n",
+      " 11  bedrooms_ratio      16512 non-null  float64\n",
+      " 12  people_per_house    16512 non-null  float64\n",
+      "dtypes: float64(12), object(1)\n",
+      "memory usage: 1.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_features.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "0cc7adae-e855-467f-b6cc-bfa666483ce8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 16512 entries, 16415 to 8967\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           16512 non-null  float64\n",
+      " 1   latitude            16512 non-null  float64\n",
+      " 2   housing_median_age  16512 non-null  float64\n",
+      " 3   total_rooms         16512 non-null  float64\n",
+      " 4   total_bedrooms      16512 non-null  float64\n",
+      " 5   population          16512 non-null  float64\n",
+      " 6   households          16512 non-null  float64\n",
+      " 7   median_income       16512 non-null  float64\n",
+      " 8   identifier          16512 non-null  float64\n",
+      " 9   rooms_per_house     16512 non-null  float64\n",
+      " 10  bedrooms_ratio      16512 non-null  float64\n",
+      " 11  people_per_house    16512 non-null  float64\n",
+      "dtypes: float64(12)\n",
+      "memory usage: 1.6 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_features_numeric = train_features.select_dtypes(include=[np.number])\n",
+    "train_features_numeric.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "201dee2a-88fb-4743-878f-741b9abd2396",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "SimpleImputer(strategy='median')"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simple_imputer.fit(train_features_numeric)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "76197fd5-27cf-4e3a-814a-6f1fba1853a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.18480000e+02,  3.42500000e+01,  2.90000000e+01,  2.12900000e+03,\n",
+       "        4.36000000e+02,  1.16800000e+03,  4.11000000e+02,  3.53410000e+00,\n",
+       "       -1.18445990e+05,  5.22483203e+00,  2.03413719e-01,  2.82065903e+00])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simple_imputer.statistics_\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "38f3b792-0d3c-45c6-89ba-ac58291a4b09",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(16512, 12)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simple_imputer.transform(train_features_numeric).shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "ce34233e-17af-4caa-ab14-7c8442143e5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_features_numeric = pd.DataFrame(simple_imputer.transform(train_features_numeric),columns=train_features_numeric.columns,index=train_features_numeric.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "efa46450-ffd6-4b29-8c22-005beef95612",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#consistency\n",
+    "##estimator\n",
+    "##transformer\n",
+    "##predictors\n",
+    "\n",
+    "#inspection\n",
+    "\n",
+    "#composition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "00180e0b-4453-4359-9c3b-45ef057e5c9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>identifier</th>\n",
+       "      <th>rooms_per_house</th>\n",
+       "      <th>bedrooms_ratio</th>\n",
+       "      <th>people_per_house</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16415</th>\n",
+       "      <td>-121.26</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>465.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>3.1719</td>\n",
+       "      <td>-121222.12</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>2.752688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>-121.64</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1999.0</td>\n",
+       "      <td>415.0</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>4.4583</td>\n",
+       "      <td>-121602.15</td>\n",
+       "      <td>6.246875</td>\n",
+       "      <td>0.207604</td>\n",
+       "      <td>3.021875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7741</th>\n",
+       "      <td>-118.15</td>\n",
+       "      <td>33.95</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2753.0</td>\n",
+       "      <td>702.0</td>\n",
+       "      <td>1592.0</td>\n",
+       "      <td>614.0</td>\n",
+       "      <td>2.7875</td>\n",
+       "      <td>-118116.05</td>\n",
+       "      <td>4.483713</td>\n",
+       "      <td>0.254995</td>\n",
+       "      <td>2.592834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4425</th>\n",
+       "      <td>-118.24</td>\n",
+       "      <td>34.07</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>223.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>249.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>1.6136</td>\n",
+       "      <td>-118205.93</td>\n",
+       "      <td>2.719512</td>\n",
+       "      <td>0.358744</td>\n",
+       "      <td>3.036585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>-121.82</td>\n",
+       "      <td>38.02</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>2.2361</td>\n",
+       "      <td>-121781.98</td>\n",
+       "      <td>4.400000</td>\n",
+       "      <td>0.244318</td>\n",
+       "      <td>2.525000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1221</th>\n",
+       "      <td>-120.65</td>\n",
+       "      <td>38.28</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>3095.0</td>\n",
+       "      <td>681.0</td>\n",
+       "      <td>1341.0</td>\n",
+       "      <td>546.0</td>\n",
+       "      <td>2.1382</td>\n",
+       "      <td>-120611.72</td>\n",
+       "      <td>5.668498</td>\n",
+       "      <td>0.220032</td>\n",
+       "      <td>2.456044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12640</th>\n",
+       "      <td>-121.45</td>\n",
+       "      <td>38.53</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>1893.0</td>\n",
+       "      <td>415.0</td>\n",
+       "      <td>884.0</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>2.1679</td>\n",
+       "      <td>-121411.47</td>\n",
+       "      <td>4.792405</td>\n",
+       "      <td>0.219229</td>\n",
+       "      <td>2.237975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5747</th>\n",
+       "      <td>-118.27</td>\n",
+       "      <td>34.17</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>1560.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>269.0</td>\n",
+       "      <td>5.5118</td>\n",
+       "      <td>-118235.83</td>\n",
+       "      <td>5.799257</td>\n",
+       "      <td>0.179487</td>\n",
+       "      <td>3.066914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16224</th>\n",
+       "      <td>-121.33</td>\n",
+       "      <td>37.98</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>3113.0</td>\n",
+       "      <td>576.0</td>\n",
+       "      <td>1746.0</td>\n",
+       "      <td>544.0</td>\n",
+       "      <td>3.4625</td>\n",
+       "      <td>-121292.02</td>\n",
+       "      <td>5.722426</td>\n",
+       "      <td>0.185031</td>\n",
+       "      <td>3.209559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8967</th>\n",
+       "      <td>-118.40</td>\n",
+       "      <td>34.00</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>1534.0</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>751.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>5.4440</td>\n",
+       "      <td>-118366.00</td>\n",
+       "      <td>5.922780</td>\n",
+       "      <td>0.168188</td>\n",
+       "      <td>2.899614</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16512 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "16415    -121.26     37.88                42.0        465.0            93.0   \n",
+       "1279     -121.64     37.85                22.0       1999.0           415.0   \n",
+       "7741     -118.15     33.95                35.0       2753.0           702.0   \n",
+       "4425     -118.24     34.07                27.0        223.0            80.0   \n",
+       "1283     -121.82     38.02                46.0        176.0            43.0   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "1221     -120.65     38.28                21.0       3095.0           681.0   \n",
+       "12640    -121.45     38.53                34.0       1893.0           415.0   \n",
+       "5747     -118.27     34.17                48.0       1560.0           280.0   \n",
+       "16224    -121.33     37.98                36.0       3113.0           576.0   \n",
+       "8967     -118.40     34.00                37.0       1534.0           258.0   \n",
+       "\n",
+       "       population  households  median_income  identifier  rooms_per_house  \\\n",
+       "16415       256.0        93.0         3.1719  -121222.12         5.000000   \n",
+       "1279        967.0       320.0         4.4583  -121602.15         6.246875   \n",
+       "7741       1592.0       614.0         2.7875  -118116.05         4.483713   \n",
+       "4425        249.0        82.0         1.6136  -118205.93         2.719512   \n",
+       "1283        101.0        40.0         2.2361  -121781.98         4.400000   \n",
+       "...           ...         ...            ...         ...              ...   \n",
+       "1221       1341.0       546.0         2.1382  -120611.72         5.668498   \n",
+       "12640       884.0       395.0         2.1679  -121411.47         4.792405   \n",
+       "5747        825.0       269.0         5.5118  -118235.83         5.799257   \n",
+       "16224      1746.0       544.0         3.4625  -121292.02         5.722426   \n",
+       "8967        751.0       259.0         5.4440  -118366.00         5.922780   \n",
+       "\n",
+       "       bedrooms_ratio  people_per_house  \n",
+       "16415        0.200000          2.752688  \n",
+       "1279         0.207604          3.021875  \n",
+       "7741         0.254995          2.592834  \n",
+       "4425         0.358744          3.036585  \n",
+       "1283         0.244318          2.525000  \n",
+       "...               ...               ...  \n",
+       "1221         0.220032          2.456044  \n",
+       "12640        0.219229          2.237975  \n",
+       "5747         0.179487          3.066914  \n",
+       "16224        0.185031          3.209559  \n",
+       "8967         0.168188          2.899614  \n",
+       "\n",
+       "[16512 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#this is my numerical data \n",
+    "train_features_numeric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "de11d63e-413e-4558-852c-068485f0d449",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method DataFrame.value_counts of       ocean_proximity\n",
+       "16415          INLAND\n",
+       "1279           INLAND\n",
+       "7741        <1H OCEAN\n",
+       "4425        <1H OCEAN\n",
+       "1283           INLAND\n",
+       "...               ...\n",
+       "1221           INLAND\n",
+       "12640          INLAND\n",
+       "5747        <1H OCEAN\n",
+       "16224          INLAND\n",
+       "8967        <1H OCEAN\n",
+       "\n",
+       "[16512 rows x 1 columns]>"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#   now what to do with catagories like ocean proximity\n",
+    "train_features[[\"ocean_proximity\"]].value_counts\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "4353f67b-9cd4-4d6d-8542-4910bb4b1520",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_features_categorical = train_features[[\"ocean_proximity\"]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "05ab56d3-257e-4479-9d95-49db1db56817",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OrdinalEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "83d575e4-c48b-40a4-9a21-a0bfaddc2cc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.],\n",
+       "       [1.],\n",
+       "       [0.],\n",
+       "       ...,\n",
+       "       [0.],\n",
+       "       [1.],\n",
+       "       [0.]], shape=(16512, 1))"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "oe = OrdinalEncoder()\n",
+    "ocean_proximity_index= oe.fit_transform(train_features_categorical)\n",
+    "ocean_proximity_index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "bfa4db35-e823-4f90-8979-2a63db139068",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16415</th>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7741</th>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4425</th>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1221</th>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12640</th>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5747</th>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16224</th>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8967</th>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16512 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ocean_proximity\n",
+       "16415          INLAND\n",
+       "1279           INLAND\n",
+       "7741        <1H OCEAN\n",
+       "4425        <1H OCEAN\n",
+       "1283           INLAND\n",
+       "...               ...\n",
+       "1221           INLAND\n",
+       "12640          INLAND\n",
+       "5747        <1H OCEAN\n",
+       "16224          INLAND\n",
+       "8967        <1H OCEAN\n",
+       "\n",
+       "[16512 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_features_categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "cbe182ac-c5d5-440e-9cf4-eac984d35ff0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
+       "       dtype=object)]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "oe.categories_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "f184c906-71c9-4477-b800-aff5ac69c619",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "fe21ab89-6342-44db-85cb-87e0a6c546ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ohe =OneHotEncoder()\n",
+    "ocean_proximity_ohe= ohe.fit_transform(train_features_categorical)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "f160c0ed-fbff-4564-b01a-c00c98cd0e04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 1., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0.],\n",
+       "       [1., 0., 0., 0., 0.],\n",
+       "       ...,\n",
+       "       [1., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0.],\n",
+       "       [1., 0., 0., 0., 0.]], shape=(16512, 5))"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ocean_proximity_ohe.toarray()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "310a33fc-50b1-4aff-b7d8-6a6ddf56b1e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
+       "       dtype=object)]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ohe.categories_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "92e8521b-3cbe-4ba6-98b3-7a5a82229cb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_features_categorical_ohe = pd.DataFrame(ocean_proximity_ohe.toarray(),columns=ohe.get_feature_names_out())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "2248526c-5858-4385-bbe9-75e016e27da0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "9fedd8d3-ecda-449d-8eea-7ea0483c746c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mms = MinMaxScaler(feature_range=(-1,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "33073173-49d1-4844-bedb-197d600adef7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.38446215,  0.13496281,  0.60784314, ..., -0.93691343,\n",
+       "        -0.88348775, -0.99668387],\n",
+       "       [-0.46015936,  0.12858661, -0.17647059, ..., -0.91797651,\n",
+       "        -0.87804467, -0.99625062],\n",
+       "       [ 0.23505976, -0.70031881,  0.33333333, ..., -0.94475454,\n",
+       "        -0.84412062, -0.99694115],\n",
+       "       ...,\n",
+       "       [ 0.21115538, -0.65356004,  0.84313725, ..., -0.92477472,\n",
+       "        -0.89817158, -0.99617813],\n",
+       "       [-0.39840637,  0.15621679,  0.37254902, ..., -0.92594157,\n",
+       "        -0.89420346, -0.99594855],\n",
+       "       [ 0.18525896, -0.68969182,  0.41176471, ..., -0.9228987 ,\n",
+       "        -0.90626013, -0.9964474 ]], shape=(16512, 12))"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mms.fit_transform(train_features_numeric)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "9f4473c1-dec5-44e3-a409-6589b8e96858",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import  StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "2cb9c779-599d-4619-b932-f05bf655b26a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ss = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "6b4f48d4-abf0-44b2-b066-2e34d7145c15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-8.47903024e-01,  1.06069020e+00,  1.06500612e+00, ...,\n",
+       "        -1.79381511e-01, -2.10670105e-01, -2.84533255e-02],\n",
+       "       [-1.03770326e+00,  1.04660645e+00, -5.26863260e-01, ...,\n",
+       "         3.65121948e-01, -9.60951107e-02, -3.75641043e-03],\n",
+       "       [ 7.05462097e-01, -7.84281723e-01,  5.07851836e-01, ...,\n",
+       "        -4.04841051e-01,  6.17994355e-01, -4.31193773e-02],\n",
+       "       ...,\n",
+       "       [ 6.45525179e-01, -6.81000852e-01,  1.54256693e+00, ...,\n",
+       "         1.69649412e-01, -5.19759731e-01,  3.75800155e-04],\n",
+       "       [-8.82866226e-01,  1.10763605e+00,  5.87445305e-01, ...,\n",
+       "         1.36098158e-01, -4.36232057e-01,  1.34628996e-02],\n",
+       "       [ 5.80593518e-01, -7.60808797e-01,  6.67038774e-01, ...,\n",
+       "         2.23591409e-01, -6.90020966e-01, -1.49734229e-02]],\n",
+       "      shape=(16512, 12))"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.fit_transform(train_features_numeric)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "104773af-d5af-4c79-a1a2-83f793b4ab89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_features_numeric[\"total_rooms\"].hist()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "066c7c07-0e92-4bb4-8da3-7a998c610d2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_features_numeric[\"total_rooms\"].apply(np.log).hist(bins=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "4f0a5eac-8e75-40a1-8fe0-08be95ec3c1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics.pairwise import rbf_kernel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "a055027b-460b-4ae3-920e-2fe936a80b2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[7.44658307e-03],\n",
+       "       [4.57533877e-08],\n",
+       "       [1.00000000e+00],\n",
+       "       ...,\n",
+       "       [4.57533877e-08],\n",
+       "       [9.04837418e-01],\n",
+       "       [6.70320046e-01]], shape=(16512, 1))"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rbf_kernel(train_features_numeric[[\"housing_median_age\"]],[[35]],gamma=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "56d6bcbf-2741-4590-99db-13e67449e367",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x20ee254b250>]"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax =plt.subplots()\n",
+    "ax.hist(train_features_numeric[\"housing_median_age\"],bins =30)\n",
+    "\n",
+    "housing_ages= np.linspace(train_features_numeric[\"housing_median_age\"].min(),train_features_numeric[\"housing_median_age\"].max(),1000).reshape(-1,1)\n",
+    "\n",
+    "rbf1 = rbf_kernel(housing_ages,[[35]],gamma=0.1)\n",
+    "rbf2 = rbf_kernel(housing_ages,[[35]],gamma=0.2)\n",
+    "\n",
+    "\n",
+    "ax2=ax.twinx()\n",
+    "ax2.plot(housing_ages,rbf1,color=\"black\",lw=2)\n",
+    "ax2.plot(housing_ages,rbf2,color=\"black\",lw=2,ls=\"--\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "8e0bd526-02f6-4589-b18b-d92de7bb86fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "dd9f70ec-5a71-4085-b71e-fc9bf6630a4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.41979429],\n",
+       "       [ 0.41090161],\n",
+       "       [ 0.02075259],\n",
+       "       ...,\n",
+       "       [ 1.28678181],\n",
+       "       [-1.0601948 ],\n",
+       "       [ 1.12429213]], shape=(16512, 1))"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target_values = ss.fit_transform(train_target.to_frame())\n",
+    "target_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "61055e4c-44d8-48e2-baa3-d856b7c5840c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LinearRegression()\n",
+    "model.fit(train_features_numeric[[\"median_income\"]],target_values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "a3e8491a-5663-4056-a841-5660c49ebb28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions= model.predict(train_features_numeric[[\"median_income\"]].iloc[:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "006e3d7b-03fc-4de0-82bd-833a62b8ee52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[177436.32912486],\n",
+       "       [231100.4887263 ],\n",
+       "       [161400.49038824],\n",
+       "       [112429.4417594 ],\n",
+       "       [138397.98914116]])"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.inverse_transform(predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "ea5a4bea-bec2-4232-a5c3-016e04340436",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import TransformedTargetRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "4724c6cb-a00b-4ca0-91a6-a720295e8268",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = TransformedTargetRegressor(LinearRegression(),transformer=StandardScaler())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "b3c16296-2c91-47aa-bd76-f32576db8768",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TransformedTargetRegressor(regressor=LinearRegression(),\n",
+       "                           transformer=StandardScaler())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>TransformedTargetRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.compose.TransformedTargetRegressor.html\">?<span>Documentation for TransformedTargetRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('regressor',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">regressor&nbsp;</td>\n",
+       "            <td class=\"value\">LinearRegression()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transformer',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">transformer&nbsp;</td>\n",
+       "            <td class=\"value\">StandardScaler()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>regressor: LinearRegression</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"regressor__\"><pre>LinearRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"regressor__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>transformer: StandardScaler</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"transformer__\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"transformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "TransformedTargetRegressor(regressor=LinearRegression(),\n",
+       "                           transformer=StandardScaler())"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(train_features_numeric[[\"median_income\"]],train_target.to_frame())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "06fe6b6c-a0b5-4e52-98ca-cf8d45157854",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[177436.32912486],\n",
+       "       [231100.4887263 ],\n",
+       "       [161400.49038824],\n",
+       "       [112429.4417594 ],\n",
+       "       [138397.98914116]])"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(train_features_numeric[[\"median_income\"]].iloc[:5])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "507e8580-cabe-48f0-943d-86aeff963983",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " from sklearn.preprocessing import FunctionTransformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "3366b648-1ed0-4bda-bc85-72aa56725743",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16415</th>\n",
+       "      <td>5.545177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>6.874198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7741</th>\n",
+       "      <td>7.372746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4425</th>\n",
+       "      <td>5.517453</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>4.615121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1221</th>\n",
+       "      <td>7.201171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12640</th>\n",
+       "      <td>6.784457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5747</th>\n",
+       "      <td>6.715383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16224</th>\n",
+       "      <td>7.465083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8967</th>\n",
+       "      <td>6.621406</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16512 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       population\n",
+       "16415    5.545177\n",
+       "1279     6.874198\n",
+       "7741     7.372746\n",
+       "4425     5.517453\n",
+       "1283     4.615121\n",
+       "...           ...\n",
+       "1221     7.201171\n",
+       "12640    6.784457\n",
+       "5747     6.715383\n",
+       "16224    7.465083\n",
+       "8967     6.621406\n",
+       "\n",
+       "[16512 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logaritmic_transformer = FunctionTransformer(np.log,inverse_func=np.exp)\n",
+    "logaritmic_transformer.transform(train_features_numeric[[\"population\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "e3bfbdc2-29f5-47ff-b155-4f4d5ab1b7d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rbf_transformer =FunctionTransformer(rbf_kernel,kw_args={\"Y\":[[35.0]],\"gamma\":0.1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "d38c7bbf-6940-4d78-8698-869441d1403d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[7.44658307e-03],\n",
+       "       [4.57533877e-08],\n",
+       "       [1.00000000e+00],\n",
+       "       ...,\n",
+       "       [4.57533877e-08],\n",
+       "       [9.04837418e-01],\n",
+       "       [6.70320046e-01]], shape=(16512, 1))"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rbf_transformer.transform(train_features_numeric[[\"housing_median_age\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "d345a61e-6611-46ae-800b-67f274d6db73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#duck typing preproccessing\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "8c261919-e1df-45e6-abe8-d8322ce62689",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.utils.validation import check_array ,check_is_fitted\n",
+    "from sklearn.base import BaseEstimator,TransformerMixin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "75ce256c-67dd-4f33-988c-986350eca3a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CustomStandardScaler(BaseEstimator,TransformerMixin):\n",
+    "    def __init__(self,hp=0):\n",
+    "        self.hp = hp\n",
+    "        \n",
+    "    \n",
+    "    def fit(self,X,Y=None):\n",
+    "        X = check_array(X)\n",
+    "        self.mean_of_X=X.mean(axis = 0)\n",
+    "        self.std_of_X = X.std(axis = 0)\n",
+    "        self.n_features_in_=X.shape[1]\n",
+    "        return self\n",
+    "    def transform(self,X):\n",
+    "        check_is_fitted(self)\n",
+    "        assert self.n_features_in_ ==X.shape[1] \n",
+    "        X = X -self.mean_of_X\n",
+    "        return X/self.std_of_X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "21c6e2c0-f48e-4323-a98a-d493e1b35210",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>identifier</th>\n",
+       "      <th>rooms_per_house</th>\n",
+       "      <th>bedrooms_ratio</th>\n",
+       "      <th>people_per_house</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16415</th>\n",
+       "      <td>-0.847903</td>\n",
+       "      <td>1.060690</td>\n",
+       "      <td>1.065006</td>\n",
+       "      <td>-1.003811</td>\n",
+       "      <td>-1.068653</td>\n",
+       "      <td>-1.039117</td>\n",
+       "      <td>-1.076980</td>\n",
+       "      <td>-0.369087</td>\n",
+       "      <td>-0.847609</td>\n",
+       "      <td>-0.179382</td>\n",
+       "      <td>-0.210670</td>\n",
+       "      <td>-0.028453</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>-1.037703</td>\n",
+       "      <td>1.046606</td>\n",
+       "      <td>-0.526863</td>\n",
+       "      <td>-0.292922</td>\n",
+       "      <td>-0.292301</td>\n",
+       "      <td>-0.406831</td>\n",
+       "      <td>-0.475183</td>\n",
+       "      <td>0.309799</td>\n",
+       "      <td>-1.037612</td>\n",
+       "      <td>0.365122</td>\n",
+       "      <td>-0.096095</td>\n",
+       "      <td>-0.003756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7741</th>\n",
+       "      <td>0.705462</td>\n",
+       "      <td>-0.784282</td>\n",
+       "      <td>0.507852</td>\n",
+       "      <td>0.056499</td>\n",
+       "      <td>0.399665</td>\n",
+       "      <td>0.148977</td>\n",
+       "      <td>0.304237</td>\n",
+       "      <td>-0.571951</td>\n",
+       "      <td>0.705322</td>\n",
+       "      <td>-0.404841</td>\n",
+       "      <td>0.617994</td>\n",
+       "      <td>-0.043119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4425</th>\n",
+       "      <td>0.660509</td>\n",
+       "      <td>-0.727947</td>\n",
+       "      <td>-0.128896</td>\n",
+       "      <td>-1.115959</td>\n",
+       "      <td>-1.099996</td>\n",
+       "      <td>-1.045343</td>\n",
+       "      <td>-1.106142</td>\n",
+       "      <td>-1.191467</td>\n",
+       "      <td>0.660385</td>\n",
+       "      <td>-1.175258</td>\n",
+       "      <td>2.181309</td>\n",
+       "      <td>-0.002407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>-1.127609</td>\n",
+       "      <td>1.126414</td>\n",
+       "      <td>1.383380</td>\n",
+       "      <td>-1.137740</td>\n",
+       "      <td>-1.189204</td>\n",
+       "      <td>-1.176958</td>\n",
+       "      <td>-1.217487</td>\n",
+       "      <td>-0.862948</td>\n",
+       "      <td>-1.127521</td>\n",
+       "      <td>-0.441398</td>\n",
+       "      <td>0.457122</td>\n",
+       "      <td>-0.049343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1221</th>\n",
+       "      <td>-0.543224</td>\n",
+       "      <td>1.248474</td>\n",
+       "      <td>-0.606457</td>\n",
+       "      <td>0.214989</td>\n",
+       "      <td>0.349033</td>\n",
+       "      <td>-0.074235</td>\n",
+       "      <td>0.123963</td>\n",
+       "      <td>-0.914614</td>\n",
+       "      <td>-0.542430</td>\n",
+       "      <td>0.112548</td>\n",
+       "      <td>0.091179</td>\n",
+       "      <td>-0.055669</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12640</th>\n",
+       "      <td>-0.942803</td>\n",
+       "      <td>1.365838</td>\n",
+       "      <td>0.428258</td>\n",
+       "      <td>-0.342044</td>\n",
+       "      <td>-0.292301</td>\n",
+       "      <td>-0.480642</td>\n",
+       "      <td>-0.276351</td>\n",
+       "      <td>-0.898940</td>\n",
+       "      <td>-0.942278</td>\n",
+       "      <td>-0.270037</td>\n",
+       "      <td>0.079071</td>\n",
+       "      <td>-0.075676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5747</th>\n",
+       "      <td>0.645525</td>\n",
+       "      <td>-0.681001</td>\n",
+       "      <td>1.542567</td>\n",
+       "      <td>-0.496364</td>\n",
+       "      <td>-0.617790</td>\n",
+       "      <td>-0.533110</td>\n",
+       "      <td>-0.610388</td>\n",
+       "      <td>0.865775</td>\n",
+       "      <td>0.645436</td>\n",
+       "      <td>0.169649</td>\n",
+       "      <td>-0.519760</td>\n",
+       "      <td>0.000376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16224</th>\n",
+       "      <td>-0.882866</td>\n",
+       "      <td>1.107636</td>\n",
+       "      <td>0.587445</td>\n",
+       "      <td>0.223331</td>\n",
+       "      <td>0.095875</td>\n",
+       "      <td>0.285928</td>\n",
+       "      <td>0.118661</td>\n",
+       "      <td>-0.215726</td>\n",
+       "      <td>-0.882557</td>\n",
+       "      <td>0.136098</td>\n",
+       "      <td>-0.436232</td>\n",
+       "      <td>0.013463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8967</th>\n",
+       "      <td>0.580594</td>\n",
+       "      <td>-0.760809</td>\n",
+       "      <td>0.667039</td>\n",
+       "      <td>-0.508413</td>\n",
+       "      <td>-0.670833</td>\n",
+       "      <td>-0.598918</td>\n",
+       "      <td>-0.636899</td>\n",
+       "      <td>0.829994</td>\n",
+       "      <td>0.580356</td>\n",
+       "      <td>0.223591</td>\n",
+       "      <td>-0.690021</td>\n",
+       "      <td>-0.014973</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16512 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "16415  -0.847903  1.060690            1.065006    -1.003811       -1.068653   \n",
+       "1279   -1.037703  1.046606           -0.526863    -0.292922       -0.292301   \n",
+       "7741    0.705462 -0.784282            0.507852     0.056499        0.399665   \n",
+       "4425    0.660509 -0.727947           -0.128896    -1.115959       -1.099996   \n",
+       "1283   -1.127609  1.126414            1.383380    -1.137740       -1.189204   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "1221   -0.543224  1.248474           -0.606457     0.214989        0.349033   \n",
+       "12640  -0.942803  1.365838            0.428258    -0.342044       -0.292301   \n",
+       "5747    0.645525 -0.681001            1.542567    -0.496364       -0.617790   \n",
+       "16224  -0.882866  1.107636            0.587445     0.223331        0.095875   \n",
+       "8967    0.580594 -0.760809            0.667039    -0.508413       -0.670833   \n",
+       "\n",
+       "       population  households  median_income  identifier  rooms_per_house  \\\n",
+       "16415   -1.039117   -1.076980      -0.369087   -0.847609        -0.179382   \n",
+       "1279    -0.406831   -0.475183       0.309799   -1.037612         0.365122   \n",
+       "7741     0.148977    0.304237      -0.571951    0.705322        -0.404841   \n",
+       "4425    -1.045343   -1.106142      -1.191467    0.660385        -1.175258   \n",
+       "1283    -1.176958   -1.217487      -0.862948   -1.127521        -0.441398   \n",
+       "...           ...         ...            ...         ...              ...   \n",
+       "1221    -0.074235    0.123963      -0.914614   -0.542430         0.112548   \n",
+       "12640   -0.480642   -0.276351      -0.898940   -0.942278        -0.270037   \n",
+       "5747    -0.533110   -0.610388       0.865775    0.645436         0.169649   \n",
+       "16224    0.285928    0.118661      -0.215726   -0.882557         0.136098   \n",
+       "8967    -0.598918   -0.636899       0.829994    0.580356         0.223591   \n",
+       "\n",
+       "       bedrooms_ratio  people_per_house  \n",
+       "16415       -0.210670         -0.028453  \n",
+       "1279        -0.096095         -0.003756  \n",
+       "7741         0.617994         -0.043119  \n",
+       "4425         2.181309         -0.002407  \n",
+       "1283         0.457122         -0.049343  \n",
+       "...               ...               ...  \n",
+       "1221         0.091179         -0.055669  \n",
+       "12640        0.079071         -0.075676  \n",
+       "5747        -0.519760          0.000376  \n",
+       "16224       -0.436232          0.013463  \n",
+       "8967        -0.690021         -0.014973  \n",
+       "\n",
+       "[16512 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "css = CustomStandardScaler()\n",
+    "css.fit_transform(train_features_numeric)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "858ceaf2-bad3-40c1-9c48-834b965e4cae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pipeline\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "1fb42846-aa17-47e1-bf00-60256bb30058",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline,make_pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "2eacf7d2-eb58-4c1d-a181-4dfe03c1c8d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numerical_features_pipeline = Pipeline([\n",
+    "    (\"medianImputer\", SimpleImputer(strategy=\"median\")),\n",
+    "    (\"minMaxScaler\", MinMaxScaler())\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "3bc55444-d30a-4fb1-b4f0-265a3b41efa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;minmaxscaler&#x27;, MinMaxScaler())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('steps',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">steps&nbsp;</td>\n",
+       "            <td class=\"value\">[(&#x27;simpleimputer&#x27;, ...), (&#x27;minmaxscaler&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transform_input',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">transform_input&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('memory',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">memory&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>MinMaxScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"minmaxscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_range',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_range&nbsp;</td>\n",
+       "            <td class=\"value\">(0, ...)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('clip',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">clip&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('minmaxscaler', MinMaxScaler())])"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "make_pipeline(SimpleImputer(strategy=\"median\"),\n",
+    "              MinMaxScaler())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "9ca8bdd6-c206-4e23-ab5e-e86dc9708bea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.30776892, 0.5674814 , 0.80392157, ..., 0.03154328, 0.05825613,\n",
+       "        0.00165807],\n",
+       "       [0.26992032, 0.5642933 , 0.41176471, ..., 0.04101175, 0.06097767,\n",
+       "        0.00187469],\n",
+       "       [0.61752988, 0.1498406 , 0.66666667, ..., 0.02762273, 0.07793969,\n",
+       "        0.00152942],\n",
+       "       ...,\n",
+       "       [0.60557769, 0.17321998, 0.92156863, ..., 0.03761264, 0.05091421,\n",
+       "        0.00191094],\n",
+       "       [0.30079681, 0.5781084 , 0.68627451, ..., 0.03702921, 0.05289827,\n",
+       "        0.00202573],\n",
+       "       [0.59262948, 0.15515409, 0.70588235, ..., 0.03855065, 0.04686993,\n",
+       "        0.0017763 ]], shape=(16512, 12))"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numerical_features_pipeline.fit_transform(train_features_numeric)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "1622d313-11af-4dc9-aa7a-1d710752c227",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import ColumnTransformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "255387eb-6576-43fa-a653-9a506823191a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
+       "       'total_bedrooms', 'population', 'households', 'median_income',\n",
+       "       'ocean_proximity', 'identifier', 'rooms_per_house', 'bedrooms_ratio',\n",
+       "       'people_per_house'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_features.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "940ba306-9fe9-4f5e-9ad6-a54d2aa495c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['longitude',\n",
+       " 'latitude',\n",
+       " 'housing_median_age',\n",
+       " 'total_rooms',\n",
+       " 'total_bedrooms',\n",
+       " 'population',\n",
+       " 'households',\n",
+       " 'median_income',\n",
+       " 'identifier',\n",
+       " 'rooms_per_house',\n",
+       " 'bedrooms_ratio',\n",
+       " 'people_per_house']"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numerical_features = list(train_features.columns)\n",
+    "numerical_features.remove(\"ocean_proximity\")\n",
+    "numerical_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "14a63bb8-8ccd-4277-be13-95ae6bad7f11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "categorical_features = [\"ocean_proximity\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "d256da31-0f14-4f9f-a4fc-3a1ea079e2f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numerical_features_pipeline = Pipeline([\n",
+    "    (\"medianImputer\", SimpleImputer(strategy=\"median\")),\n",
+    "    (\"minMaxScaler\", MinMaxScaler())\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "a39ad0de-780c-4604-827e-4f41d44d4d67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "categorical_features_pipeline = Pipeline([(\"mfImputer\",SimpleImputer(strategy=\"most_frequent\" )),(\"ohe\",OneHotEncoder())])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "4a0c6e5e-70d2-47a9-800f-43626b345fba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_transfromation = ColumnTransformer([(\"numerical\",numerical_features_pipeline,numerical_features),\n",
+    "                                             (\"categorical\",categorical_features_pipeline,categorical_features)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "18f7e583-50ec-4292-bdda-e0cffafb4f55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.30776892, 0.5674814 , 0.80392157, ..., 0.        , 0.        ,\n",
+       "        0.        ],\n",
+       "       [0.26992032, 0.5642933 , 0.41176471, ..., 0.        , 0.        ,\n",
+       "        0.        ],\n",
+       "       [0.61752988, 0.1498406 , 0.66666667, ..., 0.        , 0.        ,\n",
+       "        0.        ],\n",
+       "       ...,\n",
+       "       [0.60557769, 0.17321998, 0.92156863, ..., 0.        , 0.        ,\n",
+       "        0.        ],\n",
+       "       [0.30079681, 0.5781084 , 0.68627451, ..., 0.        , 0.        ,\n",
+       "        0.        ],\n",
+       "       [0.59262948, 0.15515409, 0.70588235, ..., 0.        , 0.        ,\n",
+       "        0.        ]], shape=(16512, 17))"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_transfromation.fit_transform(train_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "16411b8c-aa48-4d1a-a966-3d319accd33d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['numerical__longitude', 'numerical__latitude',\n",
+       "       'numerical__housing_median_age', 'numerical__total_rooms',\n",
+       "       'numerical__total_bedrooms', 'numerical__population',\n",
+       "       'numerical__households', 'numerical__median_income',\n",
+       "       'numerical__identifier', 'numerical__rooms_per_house',\n",
+       "       'numerical__bedrooms_ratio', 'numerical__people_per_house',\n",
+       "       'categorical__ocean_proximity_<1H OCEAN',\n",
+       "       'categorical__ocean_proximity_INLAND',\n",
+       "       'categorical__ocean_proximity_ISLAND',\n",
+       "       'categorical__ocean_proximity_NEAR BAY',\n",
+       "       'categorical__ocean_proximity_NEAR OCEAN'], dtype=object)"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_transfromation.get_feature_names_out()\n",
+    "#\"drop\",\"passthough\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "9ac27757-0b78-47a3-838e-5b47bd9a1cfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uinstall scikit-learn\n",
+    "#!pip install scikit-learn==1.2.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "a3e95b20-5fe1-47fe-9819-3fbd3ab096a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#all of the transfromations "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "d926427c-e499-4e79-b72b-abe26e4eb2ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data,test_data  =train_test_split(df,test_size=0.2,stratify=df[\"median_income_categories\"],random_state=40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "56d2d19a-0796-4a97-a087-0138aca27561",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = train_data.drop(columns=[\"median_income_categories\"])\n",
+    "test_data = test_data.drop(columns=[\"median_income_categories\"])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "d4c2f649-ce1c-4a5d-a50b-5b0c2617eae4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_features= train_data.drop(columns=\"median_house_value\")\n",
+    "train_target= train_data[\"median_house_value\"]\n",
+    "test_features = test_data.drop(columns=\"median_house_value\")\n",
+    "test_target=test_data[\"median_house_value\"]\n",
+    "train_features = train_features.drop(columns=\"identifier\")\n",
+    "test_features = test_features.drop(columns=\"identifier\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "2f60e541-d2a8-48a9-b68e-c41b9e056c62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16415</th>\n",
+       "      <td>-121.26</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>465.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>3.1719</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>-121.64</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1999.0</td>\n",
+       "      <td>415.0</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>4.4583</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7741</th>\n",
+       "      <td>-118.15</td>\n",
+       "      <td>33.95</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2753.0</td>\n",
+       "      <td>702.0</td>\n",
+       "      <td>1592.0</td>\n",
+       "      <td>614.0</td>\n",
+       "      <td>2.7875</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4425</th>\n",
+       "      <td>-118.24</td>\n",
+       "      <td>34.07</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>223.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>249.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>1.6136</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>-121.82</td>\n",
+       "      <td>38.02</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>2.2361</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1221</th>\n",
+       "      <td>-120.65</td>\n",
+       "      <td>38.28</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>3095.0</td>\n",
+       "      <td>681.0</td>\n",
+       "      <td>1341.0</td>\n",
+       "      <td>546.0</td>\n",
+       "      <td>2.1382</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12640</th>\n",
+       "      <td>-121.45</td>\n",
+       "      <td>38.53</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>1893.0</td>\n",
+       "      <td>415.0</td>\n",
+       "      <td>884.0</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>2.1679</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5747</th>\n",
+       "      <td>-118.27</td>\n",
+       "      <td>34.17</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>1560.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>269.0</td>\n",
+       "      <td>5.5118</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16224</th>\n",
+       "      <td>-121.33</td>\n",
+       "      <td>37.98</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>3113.0</td>\n",
+       "      <td>576.0</td>\n",
+       "      <td>1746.0</td>\n",
+       "      <td>544.0</td>\n",
+       "      <td>3.4625</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8967</th>\n",
+       "      <td>-118.40</td>\n",
+       "      <td>34.00</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>1534.0</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>751.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>5.4440</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16512 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "16415    -121.26     37.88                42.0        465.0            93.0   \n",
+       "1279     -121.64     37.85                22.0       1999.0           415.0   \n",
+       "7741     -118.15     33.95                35.0       2753.0           702.0   \n",
+       "4425     -118.24     34.07                27.0        223.0            80.0   \n",
+       "1283     -121.82     38.02                46.0        176.0            43.0   \n",
+       "...          ...       ...                 ...          ...             ...   \n",
+       "1221     -120.65     38.28                21.0       3095.0           681.0   \n",
+       "12640    -121.45     38.53                34.0       1893.0           415.0   \n",
+       "5747     -118.27     34.17                48.0       1560.0           280.0   \n",
+       "16224    -121.33     37.98                36.0       3113.0           576.0   \n",
+       "8967     -118.40     34.00                37.0       1534.0           258.0   \n",
+       "\n",
+       "       population  households  median_income ocean_proximity  \n",
+       "16415       256.0        93.0         3.1719          INLAND  \n",
+       "1279        967.0       320.0         4.4583          INLAND  \n",
+       "7741       1592.0       614.0         2.7875       <1H OCEAN  \n",
+       "4425        249.0        82.0         1.6136       <1H OCEAN  \n",
+       "1283        101.0        40.0         2.2361          INLAND  \n",
+       "...           ...         ...            ...             ...  \n",
+       "1221       1341.0       546.0         2.1382          INLAND  \n",
+       "12640       884.0       395.0         2.1679          INLAND  \n",
+       "5747        825.0       269.0         5.5118       <1H OCEAN  \n",
+       "16224      1746.0       544.0         3.4625          INLAND  \n",
+       "8967        751.0       259.0         5.4440       <1H OCEAN  \n",
+       "\n",
+       "[16512 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "e68c1108-b4b4-4036-8a50-f95fc01d3e1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heavy_tail_pipeline = make_pipeline(\n",
+    "    SimpleImputer(strategy=\"median\"),\n",
+    "    FunctionTransformer(np.log,feature_names_out=\"one-to-one\"),\n",
+    "    StandardScaler()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "bfa59e21-9fe5-4c64-812c-77e3c0c1c8c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "categorical_features_pipeline = Pipeline([(\"mfImputer\",SimpleImputer(strategy=\"most_frequent\" )),(\"ohe\",OneHotEncoder())])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "6df3b712-7c84-47ba-a3a3-96c30b7ab49a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_median_age_rbf_transformer =FunctionTransformer(rbf_kernel,feature_names_out=\"one-to-one\",kw_args={\"Y\":[[35.0]],\"gamma\":0.1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "c92ffe98-87db-4516-8db4-6da655f450a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "default_numerical_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n",
+    "                                           StandardScaler())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "f83125b5-ceb3-4568-ae7a-f4b9622be394",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ratio_of_columns(X):\n",
+    "    return X[:,[0]] / X[:,[1]]\n",
+    "\n",
+    "def ratio_of_columns_name(function_transformer,features_names_in):\n",
+    "    return[\"ratio\"]\n",
+    "\n",
+    "def ratio_of_columns_pipeline():\n",
+    "    return make_pipeline(\n",
+    "        SimpleImputer(strategy=\"median\"),\n",
+    "        FunctionTransformer(ratio_of_columns,feature_names_out=ratio_of_columns_name),\n",
+    "        StandardScaler()\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "c99b8b38-a4b5-4479-bcb9-8606f161965e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_transformations = ColumnTransformer([(\"bedrooms\",ratio_of_columns_pipeline(),[\"total_bedrooms\",\"total_rooms\"]),\n",
+    "                                        (\"rooms_per_house\",ratio_of_columns_pipeline(),[\"total_rooms\",\"households\"]),\n",
+    "                                        (\"people_per_house\",ratio_of_columns_pipeline(),[\"population\",\"households\"]),\n",
+    "                                        (\"log\",heavy_tail_pipeline,[\"total_bedrooms\",\"total_rooms\",\"population\",\"households\",\"median_income\"]),\n",
+    "                                        (\"rbf\",housing_median_age_rbf_transformer,[\"housing_median_age\"]),\n",
+    "                                        (\"cat\",categorical_features_pipeline,[\"ocean_proximity\"]),\n",
+    "                                        ],remainder=default_numerical_pipeline)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "dd5b6f0b-03b0-423d-9657-8aec3e20ab10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_features_transformed=all_transformations.fit_transform(train_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "ded16497-d6cb-46a3-acd7-b2bdbbfe95f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(16512, 16)"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_features_transformed.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "2388325a-5105-4643-b411-ba300f54957c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['bedrooms__ratio', 'rooms_per_house__ratio',\n",
+       "       'people_per_house__ratio', 'log__total_bedrooms',\n",
+       "       'log__total_rooms', 'log__population', 'log__households',\n",
+       "       'log__median_income', 'rbf__housing_median_age',\n",
+       "       'cat__ocean_proximity_<1H OCEAN', 'cat__ocean_proximity_INLAND',\n",
+       "       'cat__ocean_proximity_ISLAND', 'cat__ocean_proximity_NEAR BAY',\n",
+       "       'cat__ocean_proximity_NEAR OCEAN', 'remainder__longitude',\n",
+       "       'remainder__latitude'], dtype=object)"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_transformations.get_feature_names_out()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "90113f60-1c2d-4a1e-8417-4342e66344fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-2.10670105e-01, -1.79381511e-01, -2.84533255e-02, ...,\n",
+       "         0.00000000e+00, -8.47903024e-01,  1.06069020e+00],\n",
+       "       [-9.60951107e-02,  3.65121948e-01, -3.75641043e-03, ...,\n",
+       "         0.00000000e+00, -1.03770326e+00,  1.04660645e+00],\n",
+       "       [ 6.17994355e-01, -4.04841051e-01, -4.31193773e-02, ...,\n",
+       "         0.00000000e+00,  7.05462097e-01, -7.84281723e-01],\n",
+       "       ...,\n",
+       "       [-5.19759731e-01,  1.69649412e-01,  3.75800155e-04, ...,\n",
+       "         0.00000000e+00,  6.45525179e-01, -6.81000852e-01],\n",
+       "       [-4.36232057e-01,  1.36098158e-01,  1.34628996e-02, ...,\n",
+       "         0.00000000e+00, -8.82866226e-01,  1.10763605e+00],\n",
+       "       [-6.90020966e-01,  2.23591409e-01, -1.49734229e-02, ...,\n",
+       "         0.00000000e+00,  5.80593518e-01, -7.60808797e-01]],\n",
+       "      shape=(16512, 16))"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_features_transformed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "b8cd1cea-b0e4-4b12-be24-dcd81a7022d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "ca912536-0fe2-419f-9a53-d396a067d611",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linear_regression_pipeline= make_pipeline(all_transformations,LinearRegression())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "5b57a494-8526-40ef-b1a5-609f9c5d7777",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                             (&#x27;standardscaler&#x27;,\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;functiontransformer&#x27;,\n",
+       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_of_columns_...\n",
+       "                                                   &#x27;median_income&#x27;]),\n",
+       "                                                 (&#x27;rbf&#x27;,\n",
+       "                                                  FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;,\n",
+       "                                                                      func=&lt;function rbf_kernel at 0x0000020EDECC1300&gt;,\n",
+       "                                                                      kw_args={&#x27;Y&#x27;: [[35.0]],\n",
+       "                                                                               &#x27;gamma&#x27;: 0.1}),\n",
+       "                                                  [&#x27;housing_median_age&#x27;]),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;mfImputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;ohe&#x27;,\n",
+       "                                                                   OneHotEncoder())]),\n",
+       "                                                  [&#x27;ocean_proximity&#x27;])])),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('steps',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">steps&nbsp;</td>\n",
+       "            <td class=\"value\">[(&#x27;columntransformer&#x27;, ...), (&#x27;linearregression&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transform_input',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">transform_input&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('memory',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">memory&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>columntransformer: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transformers',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">transformers&nbsp;</td>\n",
+       "            <td class=\"value\">[(&#x27;bedrooms&#x27;, ...), (&#x27;rooms_per_house&#x27;, ...), ...]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('remainder',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">remainder&nbsp;</td>\n",
+       "            <td class=\"value\">Pipeline(step...ardScaler())])</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">sparse_threshold&nbsp;</td>\n",
+       "            <td class=\"value\">0.3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transformer_weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">transformer_weights&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose_feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('force_int_remainder_cols',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">force_int_remainder_cols&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>bedrooms</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ratio_of_columns</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618040&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618720&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>rooms_per_house</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ratio_of_columns</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618040&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618720&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>people_per_house</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ratio_of_columns</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618040&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618720&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>log</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>log</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;ufunc &#x27;log&#x27;&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;one-to-one&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>rbf</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rbf__\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>rbf_kernel</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rbf__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rbf...0020EDECC1300&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;one-to-one&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">{&#x27;Y&#x27;: [[35.0]], &#x27;gamma&#x27;: 0.1}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__\"><pre>[&#x27;ocean_proximity&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__mfImputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;most_frequent&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__ohe__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('categories',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">categories&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('drop',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">drop&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sparse_output',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">sparse_output&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dtype',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">dtype&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;class &#x27;numpy.float64&#x27;&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('handle_unknown',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">handle_unknown&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_frequency',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_frequency&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_categories',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_categories&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_name_combiner',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_name_combiner&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;concat&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__\"><pre>[&#x27;longitude&#x27;, &#x27;latitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"linearregression__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('columntransformer',\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+       "                                                              SimpleImputer(strategy='median')),\n",
+       "                                                             ('standardscaler',\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[('bedrooms',\n",
+       "                                                  Pipeline(steps=[('simpleimputer',\n",
+       "                                                                   SimpleImputer(strategy='median')),\n",
+       "                                                                  ('functiontransformer',\n",
+       "                                                                   FunctionTransformer(feature_names_out=<function ratio_of_columns_...\n",
+       "                                                   'median_income']),\n",
+       "                                                 ('rbf',\n",
+       "                                                  FunctionTransformer(feature_names_out='one-to-one',\n",
+       "                                                                      func=<function rbf_kernel at 0x0000020EDECC1300>,\n",
+       "                                                                      kw_args={'Y': [[35.0]],\n",
+       "                                                                               'gamma': 0.1}),\n",
+       "                                                  ['housing_median_age']),\n",
+       "                                                 ('cat',\n",
+       "                                                  Pipeline(steps=[('mfImputer',\n",
+       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
+       "                                                                  ('ohe',\n",
+       "                                                                   OneHotEncoder())]),\n",
+       "                                                  ['ocean_proximity'])])),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linear_regression_pipeline.fit(train_features,train_target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "9af39050-b4b7-400a-a9b8-d76fe0a49519",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "house_value_prediction = linear_regression_pipeline.predict(train_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "84cde9e7-adb7-43b9-80a1-95c70ced91d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([158300., 253900., 209000., ..., 354700.,  84600., 336000.],\n",
+       "      shape=(16512,))"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_target.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "71b3f93d-3ec5-4ea0-9703-87d138135b41",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([151110.09075875, 201490.43908191, 203196.66863746, ...,\n",
+       "       277963.22776272, 140863.66957838, 289470.80942827], shape=(16512,))"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "house_value_prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "0834ef77-ef54-4417-8702-77c3dfcc3ede",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_absolute_error,mean_squared_error,root_mean_squared_error,r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "90d439f0-6c9f-4df0-9911-a8fb173b1032",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "53721.057075531244"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(train_target.values,house_value_prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "ef1b6980-f0c7-434b-8d8b-74e644f1d20c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5169319861.141138"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_squared_error(train_target.values,house_value_prediction,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "2bc388a4-395b-48d6-a011-92e00306a026",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "71897.98231620369"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "root_mean_squared_error(train_target,house_value_prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "f4d2a47c-483a-434c-9920-b7ce13c95637",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.609697114427217"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(train_target,house_value_prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "339af2aa-b1aa-4ce8-8b6b-3e5353be9552",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "0785ef76-47cc-469b-8d08-37e27a0f70b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt_regression_pipeline= make_pipeline(all_transformations,DecisionTreeRegressor())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "71c11a4b-89ce-4d01-9bf4-f442b5a0493f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                             (&#x27;standardscaler&#x27;,\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;functiontransformer&#x27;,\n",
+       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_of_columns_...\n",
+       "                                                   &#x27;median_income&#x27;]),\n",
+       "                                                 (&#x27;rbf&#x27;,\n",
+       "                                                  FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;,\n",
+       "                                                                      func=&lt;function rbf_kernel at 0x0000020EDECC1300&gt;,\n",
+       "                                                                      kw_args={&#x27;Y&#x27;: [[35.0]],\n",
+       "                                                                               &#x27;gamma&#x27;: 0.1}),\n",
+       "                                                  [&#x27;housing_median_age&#x27;]),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;mfImputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;ohe&#x27;,\n",
+       "                                                                   OneHotEncoder())]),\n",
+       "                                                  [&#x27;ocean_proximity&#x27;])])),\n",
+       "                (&#x27;decisiontreeregressor&#x27;, DecisionTreeRegressor())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('steps',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">steps&nbsp;</td>\n",
+       "            <td class=\"value\">[(&#x27;columntransformer&#x27;, ...), (&#x27;decisiontreeregressor&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transform_input',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">transform_input&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('memory',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">memory&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>columntransformer: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transformers',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">transformers&nbsp;</td>\n",
+       "            <td class=\"value\">[(&#x27;bedrooms&#x27;, ...), (&#x27;rooms_per_house&#x27;, ...), ...]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('remainder',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">remainder&nbsp;</td>\n",
+       "            <td class=\"value\">Pipeline(step...ardScaler())])</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">sparse_threshold&nbsp;</td>\n",
+       "            <td class=\"value\">0.3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transformer_weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">transformer_weights&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose_feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('force_int_remainder_cols',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">force_int_remainder_cols&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>bedrooms</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ratio_of_columns</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618040&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618720&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>rooms_per_house</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ratio_of_columns</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618040&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618720&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>people_per_house</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ratio_of_columns</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618040&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618720&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>log</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" ><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>log</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;ufunc &#x27;log&#x27;&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;one-to-one&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>rbf</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rbf__\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-57\" type=\"checkbox\" ><label for=\"sk-estimator-id-57\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>rbf_kernel</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rbf__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rbf...0020EDECC1300&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;one-to-one&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">{&#x27;Y&#x27;: [[35.0]], &#x27;gamma&#x27;: 0.1}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__\"><pre>[&#x27;ocean_proximity&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__mfImputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;most_frequent&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__ohe__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('categories',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">categories&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('drop',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">drop&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sparse_output',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">sparse_output&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dtype',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">dtype&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;class &#x27;numpy.float64&#x27;&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('handle_unknown',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">handle_unknown&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_frequency',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_frequency&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_categories',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_categories&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_name_combiner',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_name_combiner&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;concat&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__\"><pre>[&#x27;longitude&#x27;, &#x27;latitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-62\" type=\"checkbox\" ><label for=\"sk-estimator-id-62\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-64\" type=\"checkbox\" ><label for=\"sk-estimator-id-64\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"decisiontreeregressor__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">criterion&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">splitter&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_depth&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_features&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('columntransformer',\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+       "                                                              SimpleImputer(strategy='median')),\n",
+       "                                                             ('standardscaler',\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[('bedrooms',\n",
+       "                                                  Pipeline(steps=[('simpleimputer',\n",
+       "                                                                   SimpleImputer(strategy='median')),\n",
+       "                                                                  ('functiontransformer',\n",
+       "                                                                   FunctionTransformer(feature_names_out=<function ratio_of_columns_...\n",
+       "                                                   'median_income']),\n",
+       "                                                 ('rbf',\n",
+       "                                                  FunctionTransformer(feature_names_out='one-to-one',\n",
+       "                                                                      func=<function rbf_kernel at 0x0000020EDECC1300>,\n",
+       "                                                                      kw_args={'Y': [[35.0]],\n",
+       "                                                                               'gamma': 0.1}),\n",
+       "                                                  ['housing_median_age']),\n",
+       "                                                 ('cat',\n",
+       "                                                  Pipeline(steps=[('mfImputer',\n",
+       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
+       "                                                                  ('ohe',\n",
+       "                                                                   OneHotEncoder())]),\n",
+       "                                                  ['ocean_proximity'])])),\n",
+       "                ('decisiontreeregressor', DecisionTreeRegressor())])"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dt_regression_pipeline.fit(train_features,train_target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "e49d57b2-ff13-4dc7-8404-7e49d1b92fc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(train_target.values,dt_regression_pipeline.predict(train_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "11267ef0-6016-4384-906c-efb95c86a11d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(train_target.values,dt_regression_pipeline.predict(train_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "ea558ff7-a971-4c77-8603-0a431c49a0e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "2315fd32-7e41-4985-a461-cee52b7e46b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt_mae = -cross_val_score(dt_regression_pipeline,train_features,train_target,scoring=\"neg_mean_absolute_error\",cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "afd58135-e3ef-4b49-b50c-198bccad94e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count        5.000000\n",
+       "mean     46587.924072\n",
+       "std        692.083707\n",
+       "min      45887.026642\n",
+       "25%      45909.547381\n",
+       "50%      46673.239249\n",
+       "75%      46989.930345\n",
+       "max      47479.876741\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(dt_mae).describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "60aa4fb1-2899-4e5e-84f6-68ee1bf4cb77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "d7dc713e-3de4-4a6f-85d6-855680b14b15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#method 1 tweaking hiper params "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "fc2e70d0-3a27-44d4-bdb0-9f2fda62c01f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param_grid = {\"max_depth\":[5,10,15]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "33c81eb8-0afb-4274-b74d-af4ef6ef795c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gs = GridSearchCV(DecisionTreeRegressor(),param_grid,scoring=\"neg_mean_absolute_error\",cv=5) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "e0cef6ce-3b89-4162-aed5-bc9e166356d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeRegressor(),\n",
+       "             param_grid={&#x27;max_depth&#x27;: [5, 10, 15]},\n",
+       "             scoring=&#x27;neg_mean_absolute_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">estimator&nbsp;</td>\n",
+       "            <td class=\"value\">DecisionTreeRegressor()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('param_grid',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">param_grid&nbsp;</td>\n",
+       "            <td class=\"value\">{&#x27;max_depth&#x27;: [5, 10, ...]}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scoring',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">scoring&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;neg_mean_absolute_error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('refit',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">refit&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cv&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">pre_dispatch&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('error_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">error_score&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('return_train_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">return_train_score&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: DecisionTreeRegressor</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>DecisionTreeRegressor(max_depth=10)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">criterion&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">splitter&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_depth&nbsp;</td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_features&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5, estimator=DecisionTreeRegressor(),\n",
+       "             param_grid={'max_depth': [5, 10, 15]},\n",
+       "             scoring='neg_mean_absolute_error')"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gs.fit(train_features_transformed,train_target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "e7a4e25c-e5c4-41b8-8a4b-84f82b543a21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'max_depth': 10}"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gs.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "fd3bc8e0-7881-4920-bfa1-8fb409172c88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# method 2 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "5e8a4501-9f24-475f-8db8-61488069a2dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dt_regression_pipeline = Pipeline([(\"transformation\",all_transformations),(\"dtr\",DecisionTreeRegressor())])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "b4b1b338-9e3a-4820-994e-d318322e1370",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param_grid={\"transformation__rbf__kw_args\":[{\"Y\":[[35.0]],\"gamma\":0.1},\n",
+    "                                            {\"Y\":[[35.0]],\"gamma\":0.2}],\n",
+    "            \"dtr__max_depth\":[5,10,15]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "6163fe84-a0da-4104-b511-5c5f04fa8577",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gs = GridSearchCV(dt_regression_pipeline,param_grid,scoring=\"neg_mean_absolute_error\",cv=5) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "3422a56a-55d2-427c-a5b6-43c644ce6932",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-8 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;transformation&#x27;,\n",
+       "                                        ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                     SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                    (&#x27;standardscaler&#x27;,\n",
+       "                                                                                     StandardScaler())]),\n",
+       "                                                          transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                         (&#x27;functiontransformer&#x27;,\n",
+       "                                                                                          FunctionTransformer(feature_names_out=&lt;...\n",
+       "                                                                                                      &#x27;gamma&#x27;: 0.1}),\n",
+       "                                                                         [&#x27;housing_median_age&#x27;]),\n",
+       "                                                                        (&#x27;cat&#x27;,\n",
+       "                                                                         Pipeline(steps=[(&#x27;mfImputer&#x27;,\n",
+       "                                                                                          SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                                         (&#x27;ohe&#x27;,\n",
+       "                                                                                          OneHotEncoder())]),\n",
+       "                                                                         [&#x27;ocean_proximity&#x27;])])),\n",
+       "                                       (&#x27;dtr&#x27;, DecisionTreeRegressor())]),\n",
+       "             param_grid={&#x27;dtr__max_depth&#x27;: [5, 10, 15],\n",
+       "                         &#x27;transformation__rbf__kw_args&#x27;: [{&#x27;Y&#x27;: [[35.0]],\n",
+       "                                                           &#x27;gamma&#x27;: 0.1},\n",
+       "                                                          {&#x27;Y&#x27;: [[35.0]],\n",
+       "                                                           &#x27;gamma&#x27;: 0.2}]},\n",
+       "             scoring=&#x27;neg_mean_absolute_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">estimator&nbsp;</td>\n",
+       "            <td class=\"value\">Pipeline(step...Regressor())])</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('param_grid',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">param_grid&nbsp;</td>\n",
+       "            <td class=\"value\">{&#x27;dtr__max_depth&#x27;: [5, 10, ...], &#x27;transformation__rbf__kw_args&#x27;: [{&#x27;Y&#x27;: [[35.0]], &#x27;gamma&#x27;: 0.1}, {&#x27;Y&#x27;: [[35.0]], &#x27;gamma&#x27;: 0.2}]}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scoring',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">scoring&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;neg_mean_absolute_error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('refit',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">refit&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cv&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">pre_dispatch&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('error_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">error_score&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('return_train_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">return_train_score&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: Pipeline</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>transformation: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for transformation: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transformers',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">transformers&nbsp;</td>\n",
+       "            <td class=\"value\">[(&#x27;bedrooms&#x27;, ...), (&#x27;rooms_per_house&#x27;, ...), ...]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('remainder',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">remainder&nbsp;</td>\n",
+       "            <td class=\"value\">Pipeline(step...ardScaler())])</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">sparse_threshold&nbsp;</td>\n",
+       "            <td class=\"value\">0.3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transformer_weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">transformer_weights&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose_feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('force_int_remainder_cols',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">force_int_remainder_cols&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>bedrooms</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__bedrooms__\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__bedrooms__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-73\" type=\"checkbox\" ><label for=\"sk-estimator-id-73\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ratio_of_columns</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__bedrooms__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618040&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618720&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-74\" type=\"checkbox\" ><label for=\"sk-estimator-id-74\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__bedrooms__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-75\" type=\"checkbox\" ><label for=\"sk-estimator-id-75\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>rooms_per_house</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__rooms_per_house__\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-76\" type=\"checkbox\" ><label for=\"sk-estimator-id-76\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__rooms_per_house__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-77\" type=\"checkbox\" ><label for=\"sk-estimator-id-77\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ratio_of_columns</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__rooms_per_house__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618040&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618720&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-78\" type=\"checkbox\" ><label for=\"sk-estimator-id-78\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__rooms_per_house__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-79\" type=\"checkbox\" ><label for=\"sk-estimator-id-79\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>people_per_house</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__people_per_house__\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-80\" type=\"checkbox\" ><label for=\"sk-estimator-id-80\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__people_per_house__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ratio_of_columns</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__people_per_house__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618040&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rat...0020EE2618720&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__people_per_house__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>log</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__log__\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__log__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-85\" type=\"checkbox\" ><label for=\"sk-estimator-id-85\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>log</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__log__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;ufunc &#x27;log&#x27;&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;one-to-one&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-86\" type=\"checkbox\" ><label for=\"sk-estimator-id-86\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__log__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-87\" type=\"checkbox\" ><label for=\"sk-estimator-id-87\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>rbf</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__rbf__\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-88\" type=\"checkbox\" ><label for=\"sk-estimator-id-88\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>rbf_kernel</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__rbf__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">func&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;function rbf...0020EDECC1300&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inverse_func&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validate&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">accept_sparse&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">check_inverse&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_names_out&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;one-to-one&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">{&#x27;Y&#x27;: [[35.0]], &#x27;gamma&#x27;: 0.1}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inv_kw_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__cat__\"><pre>[&#x27;ocean_proximity&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-90\" type=\"checkbox\" ><label for=\"sk-estimator-id-90\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__cat__mfImputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;most_frequent&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-91\" type=\"checkbox\" ><label for=\"sk-estimator-id-91\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__cat__ohe__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('categories',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">categories&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('drop',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">drop&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sparse_output',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">sparse_output&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dtype',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">dtype&nbsp;</td>\n",
+       "            <td class=\"value\">&lt;class &#x27;numpy.float64&#x27;&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('handle_unknown',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">handle_unknown&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_frequency',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_frequency&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_categories',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_categories&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_name_combiner',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">feature_name_combiner&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;concat&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-92\" type=\"checkbox\" ><label for=\"sk-estimator-id-92\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__remainder__\"><pre>[&#x27;longitude&#x27;, &#x27;latitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-93\" type=\"checkbox\" ><label for=\"sk-estimator-id-93\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__remainder__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">missing_values&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">strategy&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fill_value&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-94\" type=\"checkbox\" ><label for=\"sk-estimator-id-94\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___transformation__remainder__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_mean&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">with_std&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-95\" type=\"checkbox\" ><label for=\"sk-estimator-id-95\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___dtr__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">criterion&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">splitter&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_depth&nbsp;</td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_features&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
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+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[('transformation',\n",
+       "                                        ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+       "                                                                                     SimpleImputer(strategy='median')),\n",
+       "                                                                                    ('standardscaler',\n",
+       "                                                                                     StandardScaler())]),\n",
+       "                                                          transformers=[('bedrooms',\n",
+       "                                                                         Pipeline(steps=[('simpleimputer',\n",
+       "                                                                                          SimpleImputer(strategy='median')),\n",
+       "                                                                                         ('functiontransformer',\n",
+       "                                                                                          FunctionTransformer(feature_names_out=<...\n",
+       "                                                                                                      'gamma': 0.1}),\n",
+       "                                                                         ['housing_median_age']),\n",
+       "                                                                        ('cat',\n",
+       "                                                                         Pipeline(steps=[('mfImputer',\n",
+       "                                                                                          SimpleImputer(strategy='most_frequent')),\n",
+       "                                                                                         ('ohe',\n",
+       "                                                                                          OneHotEncoder())]),\n",
+       "                                                                         ['ocean_proximity'])])),\n",
+       "                                       ('dtr', DecisionTreeRegressor())]),\n",
+       "             param_grid={'dtr__max_depth': [5, 10, 15],\n",
+       "                         'transformation__rbf__kw_args': [{'Y': [[35.0]],\n",
+       "                                                           'gamma': 0.1},\n",
+       "                                                          {'Y': [[35.0]],\n",
+       "                                                           'gamma': 0.2}]},\n",
+       "             scoring='neg_mean_absolute_error')"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gs.fit(train_features,train_target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "b65b8f79-3492-49bb-8085-c40eac0b4806",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'dtr__max_depth': 10,\n",
+       " 'transformation__rbf__kw_args': {'Y': [[35.0]], 'gamma': 0.1}}"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gs.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "8cd38b44-e2c3-445d-9acd-7d5ac0f18077",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_dtr__max_depth</th>\n",
+       "      <th>param_transformation__rbf__kw_args</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.160371</td>\n",
+       "      <td>0.017530</td>\n",
+       "      <td>0.010740</td>\n",
+       "      <td>0.002847</td>\n",
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+       "      <td>{'Y': [[35.0]], 'gamma': 0.1}</td>\n",
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+       "      <td>-41721.040952</td>\n",
+       "      <td>-41505.807454</td>\n",
+       "      <td>-42523.412811</td>\n",
+       "      <td>-41513.426356</td>\n",
+       "      <td>-41679.414331</td>\n",
+       "      <td>462.581150</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.181755</td>\n",
+       "      <td>0.019895</td>\n",
+       "      <td>0.013844</td>\n",
+       "      <td>0.003454</td>\n",
+       "      <td>10</td>\n",
+       "      <td>{'Y': [[35.0]], 'gamma': 0.2}</td>\n",
+       "      <td>{'dtr__max_depth': 10, 'transformation__rbf__k...</td>\n",
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+       "      <td>-41782.759990</td>\n",
+       "      <td>-42440.091189</td>\n",
+       "      <td>-41546.489010</td>\n",
+       "      <td>-41701.322987</td>\n",
+       "      <td>405.788342</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.269636</td>\n",
+       "      <td>0.012073</td>\n",
+       "      <td>0.015563</td>\n",
+       "      <td>0.003003</td>\n",
+       "      <td>15</td>\n",
+       "      <td>{'Y': [[35.0]], 'gamma': 0.1}</td>\n",
+       "      <td>{'dtr__max_depth': 15, 'transformation__rbf__k...</td>\n",
+       "      <td>-42936.519740</td>\n",
+       "      <td>-43306.967704</td>\n",
+       "      <td>-44693.512985</td>\n",
+       "      <td>-44638.209139</td>\n",
+       "      <td>-43741.081625</td>\n",
+       "      <td>-43863.258239</td>\n",
+       "      <td>703.292595</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.255822</td>\n",
+       "      <td>0.015210</td>\n",
+       "      <td>0.015502</td>\n",
+       "      <td>0.003107</td>\n",
+       "      <td>15</td>\n",
+       "      <td>{'Y': [[35.0]], 'gamma': 0.2}</td>\n",
+       "      <td>{'dtr__max_depth': 15, 'transformation__rbf__k...</td>\n",
+       "      <td>-43637.447268</td>\n",
+       "      <td>-43931.922106</td>\n",
+       "      <td>-44434.308660</td>\n",
+       "      <td>-45058.074285</td>\n",
+       "      <td>-43202.784627</td>\n",
+       "      <td>-44052.907389</td>\n",
+       "      <td>642.772227</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.125656</td>\n",
+       "      <td>0.016615</td>\n",
+       "      <td>0.014980</td>\n",
+       "      <td>0.002902</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'Y': [[35.0]], 'gamma': 0.1}</td>\n",
+       "      <td>{'dtr__max_depth': 5, 'transformation__rbf__kw...</td>\n",
+       "      <td>-49778.593213</td>\n",
+       "      <td>-48206.829410</td>\n",
+       "      <td>-48978.500710</td>\n",
+       "      <td>-48401.981311</td>\n",
+       "      <td>-47580.701203</td>\n",
+       "      <td>-48589.321169</td>\n",
+       "      <td>743.577718</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.114708</td>\n",
+       "      <td>0.016050</td>\n",
+       "      <td>0.014625</td>\n",
+       "      <td>0.003701</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'Y': [[35.0]], 'gamma': 0.2}</td>\n",
+       "      <td>{'dtr__max_depth': 5, 'transformation__rbf__kw...</td>\n",
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+       "      <td>-47580.701203</td>\n",
+       "      <td>-48589.321169</td>\n",
+       "      <td>743.577718</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "2       0.160371      0.017530         0.010740        0.002847   \n",
+       "3       0.181755      0.019895         0.013844        0.003454   \n",
+       "4       0.269636      0.012073         0.015563        0.003003   \n",
+       "5       0.255822      0.015210         0.015502        0.003107   \n",
+       "0       0.125656      0.016615         0.014980        0.002902   \n",
+       "1       0.114708      0.016050         0.014625        0.003701   \n",
+       "\n",
+       "   param_dtr__max_depth param_transformation__rbf__kw_args  \\\n",
+       "2                    10      {'Y': [[35.0]], 'gamma': 0.1}   \n",
+       "3                    10      {'Y': [[35.0]], 'gamma': 0.2}   \n",
+       "4                    15      {'Y': [[35.0]], 'gamma': 0.1}   \n",
+       "5                    15      {'Y': [[35.0]], 'gamma': 0.2}   \n",
+       "0                     5      {'Y': [[35.0]], 'gamma': 0.1}   \n",
+       "1                     5      {'Y': [[35.0]], 'gamma': 0.2}   \n",
+       "\n",
+       "                                              params  split0_test_score  \\\n",
+       "2  {'dtr__max_depth': 10, 'transformation__rbf__k...      -41133.384081   \n",
+       "3  {'dtr__max_depth': 10, 'transformation__rbf__k...      -41255.550113   \n",
+       "4  {'dtr__max_depth': 15, 'transformation__rbf__k...      -42936.519740   \n",
+       "5  {'dtr__max_depth': 15, 'transformation__rbf__k...      -43637.447268   \n",
+       "0  {'dtr__max_depth': 5, 'transformation__rbf__kw...      -49778.593213   \n",
+       "1  {'dtr__max_depth': 5, 'transformation__rbf__kw...      -49778.593213   \n",
+       "\n",
+       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
+       "2      -41721.040952      -41505.807454      -42523.412811      -41513.426356   \n",
+       "3      -41481.724633      -41782.759990      -42440.091189      -41546.489010   \n",
+       "4      -43306.967704      -44693.512985      -44638.209139      -43741.081625   \n",
+       "5      -43931.922106      -44434.308660      -45058.074285      -43202.784627   \n",
+       "0      -48206.829410      -48978.500710      -48401.981311      -47580.701203   \n",
+       "1      -48206.829410      -48978.500710      -48401.981311      -47580.701203   \n",
+       "\n",
+       "   mean_test_score  std_test_score  rank_test_score  \n",
+       "2    -41679.414331      462.581150                1  \n",
+       "3    -41701.322987      405.788342                2  \n",
+       "4    -43863.258239      703.292595                3  \n",
+       "5    -44052.907389      642.772227                4  \n",
+       "0    -48589.321169      743.577718                5  \n",
+       "1    -48589.321169      743.577718                5  "
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(gs.cv_results_).sort_values(by=\"mean_test_score\",ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "5c0b6589-492b-449f-ab9a-2784d9ea11c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import randint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "68cec96c-5410-4631-bfc2-27a6ab3bb9ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<scipy.stats._distn_infrastructure.rv_discrete_frozen at 0x20ee221b8c0>"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "randint(low=5,high=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "0a155ede-9e7f-4712-8d04-98318a91116e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_model= gs.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "0a2856cd-55cf-41f0-8c28-780e26049a88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.011, 0.016, 0.124, 0.005, 0.003, 0.004, 0.006, 0.542, 0.024,\n",
+       "       0.   , 0.168, 0.   , 0.   , 0.001, 0.051, 0.045])"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_model[\"dtr\"].feature_importances_.round(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "d9430d6d-0fcd-4dc3-a025-5c77076db518",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(np.float64(0.542), 'log__median_income'),\n",
+       " (np.float64(0.168), 'cat__ocean_proximity_INLAND'),\n",
+       " (np.float64(0.124), 'people_per_house__ratio'),\n",
+       " (np.float64(0.051), 'remainder__longitude'),\n",
+       " (np.float64(0.045), 'remainder__latitude'),\n",
+       " (np.float64(0.024), 'rbf__housing_median_age'),\n",
+       " (np.float64(0.016), 'rooms_per_house__ratio'),\n",
+       " (np.float64(0.011), 'bedrooms__ratio'),\n",
+       " (np.float64(0.006), 'log__households'),\n",
+       " (np.float64(0.005), 'log__total_bedrooms'),\n",
+       " (np.float64(0.004), 'log__population'),\n",
+       " (np.float64(0.003), 'log__total_rooms'),\n",
+       " (np.float64(0.001), 'cat__ocean_proximity_NEAR OCEAN'),\n",
+       " (np.float64(0.0), 'cat__ocean_proximity_NEAR BAY'),\n",
+       " (np.float64(0.0), 'cat__ocean_proximity_ISLAND'),\n",
+       " (np.float64(0.0), 'cat__ocean_proximity_<1H OCEAN')]"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(zip(housing_model[\"dtr\"].feature_importances_.round(3),housing_model[\"transformation\"].get_feature_names_out()),reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "5d9c0146-d0b8-4b09-9ced-a7b74041ee63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "62619.630122520095"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "root_mean_squared_error(test_target,housing_model.predict(test_features)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "98f94e6b-de26-45d0-be5c-dcc8f6a2effe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "9cccd4c4-6e4a-4c0d-baa9-86d6b981c59e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ci = 0.95"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "9df156a9-eb3e-4e8a-8715-eb9a0fe5c0ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "se = (test_target.values-housing_model.predict(test_features))**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "98f2cb3a-6bcc-4245-8100-0ee57c594c0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([59741.49293713, 65371.17235453])"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sqrt(stats.t.interval(ci,len(se)-1,loc=np.mean(se),scale=stats.sem(se)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "6a1a5cd2-ac9b-4a14-be1f-419b40d836e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import joblib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "6bdd7d43-6e4b-4c79-870e-6cf53a9bb375",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['housing_model.pkl']"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "joblib.dump(housing_model,\"housing_model.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "ebfb6fba-45a3-423b-a4f0-fd2a0818ee80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_model_loaded=joblib.load(\"housing_model.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10f1f5ba-e4ba-4e13-9fa5-c71b7854d4a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ml operations (mlops)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/projects/notebooks/housing-price-regression/housing_ml_notebook_polished.ipynb b/projects/notebooks/housing-price-regression/housing_ml_notebook_polished.ipynb
new file mode 100644
index 0000000..965b85d
--- /dev/null
+++ b/projects/notebooks/housing-price-regression/housing_ml_notebook_polished.ipynb
@@ -0,0 +1,1475 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "94659fc3",
+   "metadata": {},
+   "source": [
+    "# 🏠 California Housing Price Prediction\n",
+    "### End-to-End Machine Learning Pipeline\n",
+    "\n",
+    "This notebook walks through a complete supervised regression workflow on the\n",
+    "**California Housing Dataset**, covering every stage from raw data ingestion to\n",
+    "model evaluation and persistence.\n",
+    "\n",
+    "| Stage | Tools used |\n",
+    "|-------|-----------|\n",
+    "| Data Loading | `pandas`, `numpy` |\n",
+    "| Exploration (EDA) | `pandas`, `matplotlib`, `seaborn` |\n",
+    "| Train / Test Split | `sklearn.model_selection` |\n",
+    "| Feature Engineering | `FunctionTransformer`, custom ratio pipelines |\n",
+    "| Preprocessing | `SimpleImputer`, `OrdinalEncoder`, `OneHotEncoder`, `StandardScaler`, `MinMaxScaler` |\n",
+    "| Pipeline Assembly | `Pipeline`, `make_pipeline`, `ColumnTransformer` |\n",
+    "| Model Training | `LinearRegression`, `DecisionTreeRegressor` |\n",
+    "| Evaluation | MAE, MSE, RMSE, R², cross-validation |\n",
+    "| Hyperparameter Tuning | `GridSearchCV` |\n",
+    "| Model Persistence | `joblib` |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53c396a8",
+   "metadata": {},
+   "source": [
+    "## 1. Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dc9f5452",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Scikit-learn — split\n",
+    "from sklearn.model_selection import (\n",
+    "    train_test_split, StratifiedShuffleSplit,\n",
+    "    cross_val_score, GridSearchCV\n",
+    ")\n",
+    "\n",
+    "# Scikit-learn — preprocessing\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import (\n",
+    "    OrdinalEncoder, OneHotEncoder,\n",
+    "    MinMaxScaler, StandardScaler, FunctionTransformer\n",
+    ")\n",
+    "\n",
+    "# Scikit-learn — pipelines & composition\n",
+    "from sklearn.pipeline import Pipeline, make_pipeline\n",
+    "from sklearn.compose import ColumnTransformer, TransformedTargetRegressor\n",
+    "from sklearn.metrics.pairwise import rbf_kernel\n",
+    "\n",
+    "# Scikit-learn — models & metrics\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.metrics import (\n",
+    "    mean_absolute_error, mean_squared_error,\n",
+    "    root_mean_squared_error, r2_score\n",
+    ")\n",
+    "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "from sklearn.utils.validation import check_array, check_is_fitted\n",
+    "\n",
+    "# Stats & persistence\n",
+    "from scipy import stats\n",
+    "import joblib\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sns.set_theme(style=\"whitegrid\", palette=\"muted\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abbeaad7",
+   "metadata": {},
+   "source": [
+    "## 2. Data Loading\n",
+    "\n",
+    "We load the California housing CSV into a pandas `DataFrame`.\n",
+    "The dataset contains **20,640 rows** and **10 columns**, representing\n",
+    "census-block-level statistics for California districts.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bd5fe6d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (20640, 10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-122.23</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>452600.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-122.22</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>7099.0</td>\n",
+       "      <td>1106.0</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>1138.0</td>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>358500.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1467.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>496.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>352100.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1274.0</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>558.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>341300.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1627.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>342200.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "0    -122.23     37.88                41.0        880.0           129.0   \n",
+       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
+       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
+       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
+       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
+       "\n",
+       "   population  households  median_income  median_house_value ocean_proximity  \n",
+       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
+       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
+       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
+       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
+       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"housing.csv\")\n",
+    "print(f\"Shape: {df.shape}\")\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e4aedc80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 20640 entries, 0 to 20639\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           20640 non-null  float64\n",
+      " 1   latitude            20640 non-null  float64\n",
+      " 2   housing_median_age  20640 non-null  float64\n",
+      " 3   total_rooms         20640 non-null  float64\n",
+      " 4   total_bedrooms      20433 non-null  float64\n",
+      " 5   population          20640 non-null  float64\n",
+      " 6   households          20640 non-null  float64\n",
+      " 7   median_income       20640 non-null  float64\n",
+      " 8   median_house_value  20640 non-null  float64\n",
+      " 9   ocean_proximity     20640 non-null  object \n",
+      "dtypes: float64(9), object(1)\n",
+      "memory usage: 1.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "06f7cf67",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Target column stats ===\n",
+      "count     20640.000000\n",
+      "mean     206855.816909\n",
+      "std      115395.615874\n",
+      "min       14999.000000\n",
+      "25%      119600.000000\n",
+      "50%      179700.000000\n",
+      "75%      264725.000000\n",
+      "max      500001.000000\n",
+      "Name: median_house_value, dtype: float64\n",
+      "\n",
+      "=== ocean_proximity distribution ===\n",
+      "ocean_proximity\n",
+      "<1H OCEAN     9136\n",
+      "INLAND        6551\n",
+      "NEAR OCEAN    2658\n",
+      "NEAR BAY      2290\n",
+      "ISLAND           5\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Quick look at the target and the only categorical column\n",
+    "print(\"=== Target column stats ===\")\n",
+    "print(df[\"median_house_value\"].describe())\n",
+    "print()\n",
+    "print(\"=== ocean_proximity distribution ===\")\n",
+    "print(df[\"ocean_proximity\"].value_counts())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4be25f46",
+   "metadata": {},
+   "source": [
+    "## 3. Exploratory Data Analysis (EDA)\n",
+    "\n",
+    "Before touching a model we need to *understand* the data:\n",
+    "* **Distribution** of every feature\n",
+    "* **Geographic** spread of house prices\n",
+    "* **Correlations** with the target variable\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7aac7929",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_acc95_row0_col0, #T_acc95_row1_col0, #T_acc95_row2_col0, #T_acc95_row3_col0, #T_acc95_row5_col0, #T_acc95_row6_col0, #T_acc95_row7_col0, #T_acc95_row8_col0, #T_acc95_row8_col1, #T_acc95_row8_col2, #T_acc95_row8_col3, #T_acc95_row8_col4, #T_acc95_row8_col5, #T_acc95_row8_col6, #T_acc95_row8_col7 {\n",
+       "  background-color: #08306b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_acc95_row0_col1, #T_acc95_row0_col2, #T_acc95_row0_col3, #T_acc95_row0_col4, #T_acc95_row0_col5, #T_acc95_row0_col6, #T_acc95_row0_col7, #T_acc95_row1_col1, #T_acc95_row1_col2, #T_acc95_row1_col4, #T_acc95_row1_col5, #T_acc95_row1_col6, #T_acc95_row1_col7, #T_acc95_row2_col1, #T_acc95_row2_col2, #T_acc95_row2_col4, #T_acc95_row2_col5, #T_acc95_row2_col6, #T_acc95_row2_col7, #T_acc95_row4_col0, #T_acc95_row4_col1, #T_acc95_row4_col2, #T_acc95_row4_col4, #T_acc95_row4_col5, #T_acc95_row4_col6, #T_acc95_row6_col1, #T_acc95_row6_col2, #T_acc95_row6_col4, #T_acc95_row6_col5, #T_acc95_row6_col6, #T_acc95_row7_col1, #T_acc95_row7_col2, #T_acc95_row7_col4, #T_acc95_row7_col5, #T_acc95_row7_col6, #T_acc95_row7_col7 {\n",
+       "  background-color: #f7fbff;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_acc95_row1_col3, #T_acc95_row2_col3, #T_acc95_row3_col3, #T_acc95_row4_col3, #T_acc95_row5_col2, #T_acc95_row5_col3, #T_acc95_row6_col3, #T_acc95_row7_col3 {\n",
+       "  background-color: #f5fafe;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_acc95_row3_col1, #T_acc95_row3_col4, #T_acc95_row3_col5, #T_acc95_row3_col6, #T_acc95_row4_col7, #T_acc95_row6_col7 {\n",
+       "  background-color: #f5f9fe;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_acc95_row3_col2 {\n",
+       "  background-color: #f4f9fe;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_acc95_row3_col7 {\n",
+       "  background-color: #e7f1fa;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_acc95_row5_col1, #T_acc95_row5_col4, #T_acc95_row5_col5, #T_acc95_row5_col6 {\n",
+       "  background-color: #f6faff;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_acc95_row5_col7 {\n",
+       "  background-color: #e9f2fa;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_acc95\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_acc95_level0_col0\" class=\"col_heading level0 col0\" >count</th>\n",
+       "      <th id=\"T_acc95_level0_col1\" class=\"col_heading level0 col1\" >mean</th>\n",
+       "      <th id=\"T_acc95_level0_col2\" class=\"col_heading level0 col2\" >std</th>\n",
+       "      <th id=\"T_acc95_level0_col3\" class=\"col_heading level0 col3\" >min</th>\n",
+       "      <th id=\"T_acc95_level0_col4\" class=\"col_heading level0 col4\" >25%</th>\n",
+       "      <th id=\"T_acc95_level0_col5\" class=\"col_heading level0 col5\" >50%</th>\n",
+       "      <th id=\"T_acc95_level0_col6\" class=\"col_heading level0 col6\" >75%</th>\n",
+       "      <th id=\"T_acc95_level0_col7\" class=\"col_heading level0 col7\" >max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_acc95_level0_row0\" class=\"row_heading level0 row0\" >longitude</th>\n",
+       "      <td id=\"T_acc95_row0_col0\" class=\"data row0 col0\" >20640.000000</td>\n",
+       "      <td id=\"T_acc95_row0_col1\" class=\"data row0 col1\" >-119.569704</td>\n",
+       "      <td id=\"T_acc95_row0_col2\" class=\"data row0 col2\" >2.003532</td>\n",
+       "      <td id=\"T_acc95_row0_col3\" class=\"data row0 col3\" >-124.350000</td>\n",
+       "      <td id=\"T_acc95_row0_col4\" class=\"data row0 col4\" >-121.800000</td>\n",
+       "      <td id=\"T_acc95_row0_col5\" class=\"data row0 col5\" >-118.490000</td>\n",
+       "      <td id=\"T_acc95_row0_col6\" class=\"data row0 col6\" >-118.010000</td>\n",
+       "      <td id=\"T_acc95_row0_col7\" class=\"data row0 col7\" >-114.310000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_acc95_level0_row1\" class=\"row_heading level0 row1\" >latitude</th>\n",
+       "      <td id=\"T_acc95_row1_col0\" class=\"data row1 col0\" >20640.000000</td>\n",
+       "      <td id=\"T_acc95_row1_col1\" class=\"data row1 col1\" >35.631861</td>\n",
+       "      <td id=\"T_acc95_row1_col2\" class=\"data row1 col2\" >2.135952</td>\n",
+       "      <td id=\"T_acc95_row1_col3\" class=\"data row1 col3\" >32.540000</td>\n",
+       "      <td id=\"T_acc95_row1_col4\" class=\"data row1 col4\" >33.930000</td>\n",
+       "      <td id=\"T_acc95_row1_col5\" class=\"data row1 col5\" >34.260000</td>\n",
+       "      <td id=\"T_acc95_row1_col6\" class=\"data row1 col6\" >37.710000</td>\n",
+       "      <td id=\"T_acc95_row1_col7\" class=\"data row1 col7\" >41.950000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_acc95_level0_row2\" class=\"row_heading level0 row2\" >housing_median_age</th>\n",
+       "      <td id=\"T_acc95_row2_col0\" class=\"data row2 col0\" >20640.000000</td>\n",
+       "      <td id=\"T_acc95_row2_col1\" class=\"data row2 col1\" >28.639486</td>\n",
+       "      <td id=\"T_acc95_row2_col2\" class=\"data row2 col2\" >12.585558</td>\n",
+       "      <td id=\"T_acc95_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
+       "      <td id=\"T_acc95_row2_col4\" class=\"data row2 col4\" >18.000000</td>\n",
+       "      <td id=\"T_acc95_row2_col5\" class=\"data row2 col5\" >29.000000</td>\n",
+       "      <td id=\"T_acc95_row2_col6\" class=\"data row2 col6\" >37.000000</td>\n",
+       "      <td id=\"T_acc95_row2_col7\" class=\"data row2 col7\" >52.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_acc95_level0_row3\" class=\"row_heading level0 row3\" >total_rooms</th>\n",
+       "      <td id=\"T_acc95_row3_col0\" class=\"data row3 col0\" >20640.000000</td>\n",
+       "      <td id=\"T_acc95_row3_col1\" class=\"data row3 col1\" >2635.763081</td>\n",
+       "      <td id=\"T_acc95_row3_col2\" class=\"data row3 col2\" >2181.615252</td>\n",
+       "      <td id=\"T_acc95_row3_col3\" class=\"data row3 col3\" >2.000000</td>\n",
+       "      <td id=\"T_acc95_row3_col4\" class=\"data row3 col4\" >1447.750000</td>\n",
+       "      <td id=\"T_acc95_row3_col5\" class=\"data row3 col5\" >2127.000000</td>\n",
+       "      <td id=\"T_acc95_row3_col6\" class=\"data row3 col6\" >3148.000000</td>\n",
+       "      <td id=\"T_acc95_row3_col7\" class=\"data row3 col7\" >39320.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_acc95_level0_row4\" class=\"row_heading level0 row4\" >total_bedrooms</th>\n",
+       "      <td id=\"T_acc95_row4_col0\" class=\"data row4 col0\" >20433.000000</td>\n",
+       "      <td id=\"T_acc95_row4_col1\" class=\"data row4 col1\" >537.870553</td>\n",
+       "      <td id=\"T_acc95_row4_col2\" class=\"data row4 col2\" >421.385070</td>\n",
+       "      <td id=\"T_acc95_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
+       "      <td id=\"T_acc95_row4_col4\" class=\"data row4 col4\" >296.000000</td>\n",
+       "      <td id=\"T_acc95_row4_col5\" class=\"data row4 col5\" >435.000000</td>\n",
+       "      <td id=\"T_acc95_row4_col6\" class=\"data row4 col6\" >647.000000</td>\n",
+       "      <td id=\"T_acc95_row4_col7\" class=\"data row4 col7\" >6445.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_acc95_level0_row5\" class=\"row_heading level0 row5\" >population</th>\n",
+       "      <td id=\"T_acc95_row5_col0\" class=\"data row5 col0\" >20640.000000</td>\n",
+       "      <td id=\"T_acc95_row5_col1\" class=\"data row5 col1\" >1425.476744</td>\n",
+       "      <td id=\"T_acc95_row5_col2\" class=\"data row5 col2\" >1132.462122</td>\n",
+       "      <td id=\"T_acc95_row5_col3\" class=\"data row5 col3\" >3.000000</td>\n",
+       "      <td id=\"T_acc95_row5_col4\" class=\"data row5 col4\" >787.000000</td>\n",
+       "      <td id=\"T_acc95_row5_col5\" class=\"data row5 col5\" >1166.000000</td>\n",
+       "      <td id=\"T_acc95_row5_col6\" class=\"data row5 col6\" >1725.000000</td>\n",
+       "      <td id=\"T_acc95_row5_col7\" class=\"data row5 col7\" >35682.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_acc95_level0_row6\" class=\"row_heading level0 row6\" >households</th>\n",
+       "      <td id=\"T_acc95_row6_col0\" class=\"data row6 col0\" >20640.000000</td>\n",
+       "      <td id=\"T_acc95_row6_col1\" class=\"data row6 col1\" >499.539680</td>\n",
+       "      <td id=\"T_acc95_row6_col2\" class=\"data row6 col2\" >382.329753</td>\n",
+       "      <td id=\"T_acc95_row6_col3\" class=\"data row6 col3\" >1.000000</td>\n",
+       "      <td id=\"T_acc95_row6_col4\" class=\"data row6 col4\" >280.000000</td>\n",
+       "      <td id=\"T_acc95_row6_col5\" class=\"data row6 col5\" >409.000000</td>\n",
+       "      <td id=\"T_acc95_row6_col6\" class=\"data row6 col6\" >605.000000</td>\n",
+       "      <td id=\"T_acc95_row6_col7\" class=\"data row6 col7\" >6082.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_acc95_level0_row7\" class=\"row_heading level0 row7\" >median_income</th>\n",
+       "      <td id=\"T_acc95_row7_col0\" class=\"data row7 col0\" >20640.000000</td>\n",
+       "      <td id=\"T_acc95_row7_col1\" class=\"data row7 col1\" >3.870671</td>\n",
+       "      <td id=\"T_acc95_row7_col2\" class=\"data row7 col2\" >1.899822</td>\n",
+       "      <td id=\"T_acc95_row7_col3\" class=\"data row7 col3\" >0.499900</td>\n",
+       "      <td id=\"T_acc95_row7_col4\" class=\"data row7 col4\" >2.563400</td>\n",
+       "      <td id=\"T_acc95_row7_col5\" class=\"data row7 col5\" >3.534800</td>\n",
+       "      <td id=\"T_acc95_row7_col6\" class=\"data row7 col6\" >4.743250</td>\n",
+       "      <td id=\"T_acc95_row7_col7\" class=\"data row7 col7\" >15.000100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_acc95_level0_row8\" class=\"row_heading level0 row8\" >median_house_value</th>\n",
+       "      <td id=\"T_acc95_row8_col0\" class=\"data row8 col0\" >20640.000000</td>\n",
+       "      <td id=\"T_acc95_row8_col1\" class=\"data row8 col1\" >206855.816909</td>\n",
+       "      <td id=\"T_acc95_row8_col2\" class=\"data row8 col2\" >115395.615874</td>\n",
+       "      <td id=\"T_acc95_row8_col3\" class=\"data row8 col3\" >14999.000000</td>\n",
+       "      <td id=\"T_acc95_row8_col4\" class=\"data row8 col4\" >119600.000000</td>\n",
+       "      <td id=\"T_acc95_row8_col5\" class=\"data row8 col5\" >179700.000000</td>\n",
+       "      <td id=\"T_acc95_row8_col6\" class=\"data row8 col6\" >264725.000000</td>\n",
+       "      <td id=\"T_acc95_row8_col7\" class=\"data row8 col7\" >500001.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x23037a5d940>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T.style.background_gradient(cmap=\"Blues\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4aea7b71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x1000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ── 3.1  Histograms of all numeric features ──────────────────────────────────\n",
+    "df.hist(bins=40, figsize=(14, 10), color=\"steelblue\", edgecolor=\"white\")\n",
+    "plt.suptitle(\"Distribution of All Numeric Features\", y=1.01, fontsize=14)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "03ec02cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ── 3.2  Geographic scatter (population-weighted, colour = price) ─────────────\n",
+    "fig, ax = plt.subplots(figsize=(10, 7))\n",
+    "sc = ax.scatter(\n",
+    "    df[\"longitude\"], df[\"latitude\"],\n",
+    "    s=df[\"population\"] / 100,\n",
+    "    c=df[\"median_house_value\"],\n",
+    "    cmap=\"plasma\", alpha=0.4\n",
+    ")\n",
+    "plt.colorbar(sc, ax=ax, label=\"Median House Value ($)\")\n",
+    "ax.set_xlabel(\"Longitude\"); ax.set_ylabel(\"Latitude\")\n",
+    "ax.set_title(\"California Housing Prices — Geographic Distribution\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b1be6a6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ── 3.3  Correlation with target ──────────────────────────────────────────────\n",
+    "corr = df.corr(numeric_only=True)[\"median_house_value\"].drop(\"median_house_value\").sort_values()\n",
+    "fig, ax = plt.subplots(figsize=(7, 5))\n",
+    "corr.plot.barh(ax=ax, color=[\"#d62728\" if v < 0 else \"#1f77b4\" for v in corr])\n",
+    "ax.axvline(0, color=\"black\", linewidth=0.8)\n",
+    "ax.set_title(\"Feature Correlation with Median House Value\")\n",
+    "ax.set_xlabel(\"Pearson r\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0c1edacb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ── 3.4  Median income vs house value ─────────────────────────────────────────\n",
+    "fig, ax = plt.subplots(figsize=(7, 5))\n",
+    "ax.scatter(df[\"median_income\"], df[\"median_house_value\"], alpha=0.15, s=5, color=\"steelblue\")\n",
+    "ax.set_xlabel(\"Median Income (tens of thousands $)\")\n",
+    "ax.set_ylabel(\"Median House Value ($)\")\n",
+    "ax.set_title(\"Median Income vs. Median House Value\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89eff1a5",
+   "metadata": {},
+   "source": [
+    "## 4. Train / Test Split\n",
+    "\n",
+    "We use **stratified sampling** on `median_income` so every income band is\n",
+    "represented proportionally in both sets.  This prevents a lucky (or unlucky)\n",
+    "random draw from skewing our evaluation.\n",
+    "\n",
+    "> **Why stratify?** Income is the strongest predictor (~0.69 correlation).\n",
+    "> A plain random split can accidentally over- or under-represent high-income\n",
+    "> districts, giving a misleadingly good or bad test score.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bad59bda",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Bin median_income into 5 categories for stratification\n",
+    "df[\"median_income_categories\"] = pd.cut(\n",
+    "    df[\"median_income\"],\n",
+    "    bins=[0.0, 1.5, 3.0, 4.5, 6.0, np.inf],\n",
+    "    labels=[1, 2, 3, 4, 5]\n",
+    ")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 3))\n",
+    "df[\"median_income_categories\"].value_counts().sort_index().plot.bar(ax=ax, rot=0, color=\"steelblue\")\n",
+    "ax.set_title(\"Income Category Distribution\")\n",
+    "ax.set_xlabel(\"Category\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "54226eaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train size : 16,512\n",
+      "Test  size : 4,128\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data, test_data = train_test_split(\n",
+    "    df,\n",
+    "    test_size=0.2,\n",
+    "    stratify=df[\"median_income_categories\"],\n",
+    "    random_state=40\n",
+    ")\n",
+    "\n",
+    "# Drop the helper column — it was only needed for stratification\n",
+    "train_data = train_data.drop(columns=[\"median_income_categories\"])\n",
+    "test_data  = test_data.drop(columns=[\"median_income_categories\"])\n",
+    "\n",
+    "print(f\"Train size : {len(train_data):,}\")\n",
+    "print(f\"Test  size : {len(test_data):,}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3d27b55b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature columns: ['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income', 'ocean_proximity']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Separate features from target\n",
+    "train_features = train_data.drop(columns=[\"median_house_value\"])\n",
+    "train_target   = train_data[\"median_house_value\"]\n",
+    "\n",
+    "test_features  = test_data.drop(columns=[\"median_house_value\"])\n",
+    "test_target    = test_data[\"median_house_value\"]\n",
+    "\n",
+    "# Drop the geographic identifier column if present\n",
+    "for col in [\"identifier\"]:\n",
+    "    if col in train_features.columns:\n",
+    "        train_features = train_features.drop(columns=[col])\n",
+    "        test_features  = test_features.drop(columns=[col])\n",
+    "\n",
+    "print(\"Feature columns:\", list(train_features.columns))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a46b84c0",
+   "metadata": {},
+   "source": [
+    "## 5. Feature Engineering\n",
+    "\n",
+    "Raw census features are *per block*, not *per household*.  We derive three\n",
+    "ratio features that are more semantically meaningful for predicting house value:\n",
+    "\n",
+    "| New feature | Formula | Why it matters |\n",
+    "|-------------|---------|----------------|\n",
+    "| `rooms_per_house` | `total_rooms / households` | Spaciousness of the average home |\n",
+    "| `bedrooms_ratio` | `total_bedrooms / total_rooms` | Bedroom density |\n",
+    "| `people_per_house` | `population / households` | Household crowding |\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0a6b6d6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4MTExFvtXunRp/P39zWf+69Wrx8mTJ6levbq5TY0aNVizZg3ffPPNI9Vwr9KlS1OxYsVU2/36668faz+SvegMq4g8UbVq1eKdd95h4cKFRERE8Oqrr/L000/z119/sWrVKm7evMkHH3yAjY0NTz31FG+99RZLlizB1taWFi1aEBMTw6JFi3BycqJjx44Z7r9jx46sW7eOPn36MHDgQMqUKcMvv/zC8uXL6dGjB7a2tqnWcXd3Z9myZXzwwQfUqlWLU6dOERwcTEJCgnkuINw5WxUaGsq+fftwd3e32EaePHkYOXIk48aNY8SIEXTo0IHLly+zZMkSihYtmuaZq4xwd3dn+/btPPfcc5QuXZqDBw8SHByMjY2NRY33SjnD9vnnn1OzZk2cnZ1p3749EydO5OzZs9SoUYOoqCgCAgIoX748jo6Oj1Tn/fTu3Ztt27bRu3dvfHx8ePrpp9mxYweffPIJ48aNM+/jhg0bmD17Ns2bN+fff/9l5cqVXLhwweJj+SJFinDw4EHzbZBcXFwoU6YMS5YsoXDhwuTJk4cPPvjgvnequFtmfl/udvr0aQ4dOpTmsiJFilClSpV0HZ+6devi5eXF+PHjuXDhAmXLluXTTz/l+PHjac6HTa+8efMyYsQI/Pz8yJs3L82bN+fatWssW7aM8+fPm6dCDBo0iC5dujBgwAC6du2Kvb09GzduZNeuXSxevDjT/afFxsaGYcOGMXr0aCZNmkTLli05fvy4+S4VD/rDUHIuBVYReeLefvttXF1dWb9+PbNmzeLKlSuULl2a559/noEDB1p87Dx06FBKlCjBunXr2LRpE0899RQvvfQSw4cPT1fguFeBAgVYv349/v7+zJs3j+vXr1OuXDlGjRqFj49PmusMGDCAy5cv8+GHH7J06VLKlCnDK6+8go2NDcHBweYLb3x8fJg5cyZ9+/Zl9erVqbbTsWNHChYsSHBwMIMHD6ZQoUI0adKEkSNHPvL8vNmzZzNt2jSmTZsGgKOjI1OmTOGzzz5j//79912vVatWbNu2DV9fXzp16sTkyZOZNWsWwcHB/O9//+PcuXMUL16cNm3aMHz48Cw7o1iqVCn+97//4e/vz+TJk4mPj8fR0ZEZM2aYp168+uqrxMTEsHnzZj7++GNKlSpF06ZN6datGxMnTuTkyZM4OTnRvXt3/vzzT/r378+sWbNo164dixcvZubMmYwcOZISJUrw5ptvEhkZmeren/fKzO/L3d5//33ef//9NJc1a9aM4ODgdB+jgIAAZs+ejb+/P7dv36ZFixZ07dqVrVu3pnsbaXn99dcpWLAgK1asYOPGjRQoUIDatWszf/5887SbZ599lvXr1xMQEMCYMWMwmUw4OzuzdOlSWrRo8Uj9p6Vdu3bExsaycuVKNm/eTLVq1Rg/fjzjx49P94V+krPYmDIyG19ERESeuLNnz3Lo0CFatGhhcXHWsGHDOHPmDFu2bLFidY/f559/jqurq8UZ6O+++44BAwawbds2nn32WStWJ9agM6wiIiIGlydPHnx9fWnRogWdOnUib968/PDDD+zcuZNZs2ZZu7zH7rPPPiMgIIDhw4dTpkwZoqOjWbx4MfXq1VNYzaV0hlVERCQb+O2331i6dClhYWHcvn2bqlWr0qdPH15++WVrl/bYXb58GX9/f3744QcuXbpEiRIlePHFFxk2bNhD7+wgOZMCq4iIiIgYmi61ExERERFDU2AVEREREUNTYBURERERQ9NdAsTwDh48iMlkeugNukVERCT7SExMxMbGBg8Pj4e21RlWMTyTyUR2vzbQZDKRkJCQ7fcju0tOTubUqVMP/KpSyVp6LVifxsAYNA4Ze3/XGVYxvJQzq25ublauJPNiY2MJCwvDyclJ39JiRefOnaNWrVr8888/lC5d2trl5Ep6LVifxsAYNA5w9OjRdLfVGVYRERERMTQFVhERERExNAVWERERETE0BVYRERERMTQFVhERERExNAVWERERETE0BVYRERERMTQFVhERERExNAVWERERETE0BVYRyXVsbGysXUKuZWNjg4ODg8bAijQGxqBxyBh9NauI5BopbwwODg5WriT3cnBwwNXV1dpl5GoaA2PITuOQlGwibx7rBmsFVhHJdcZ8epgzN6xdhYiI8Tk9U4hFXTysXYYCq4jkPpEXbhJ+KcnaZYiISDppDquIiIiIGJoCq4iIiIgYmgKriIiIiBiaAmsG9ezZE19fXwD27t2Li4sLMTExT6z/kJAQXFxcnlh/IiIiItami64egYeHBz/99BPFihV7Yn22adOGJk2aPLH+RERERKxNgfUR2NnZUbJkySfaZ/78+cmfP/8T7VNERETEmnLMlAAXFxc+//xzevXqhbu7Oy1btuTbb7/l22+/5cUXX6RWrVr069ePS5cumdeJiIigf//+eHh40LhxY0aNGsV///1nXp6QkMDMmTNp2LAhnp6e+Pv7k5ycbF5+75SAc+fOMXr0aBo1asRzzz1H06ZNCQgIMK8TEhKCt7c3W7ZsoWXLltSoUYPXXnuNgwcPpns/750S4OLiwieffEKfPn1wd3enSZMmBAcHW6zz888/06VLF2rWrMnzzz+Pv78/SUl3bulz69YtFi5cSIsWLXBzc6NDhw7s2rXLor+WLVuyY8cOvL29cXd3p2/fvpw/f54ZM2ZQt25dGjVqlKrPzZs307p1a9zd3WndujVr1661OHYiIiIi6ZWjzrBOnz6dKVOmMH36dGbNmsWoUaNwcnJi3rx5xMbGMmzYMJYvX87YsWM5f/483bp1o23btvj6+hIXF0dgYCBdunRh+/btFChQgOnTp/Ptt98ye/ZsypYtS1BQEPv376dChQpp9j9gwACKFy/OypUrKVSoEN999x3Tp0/Hzc2NF154AYB///2X//3vf8ybNw9bW1smT57M2LFj+frrrzP99Wxz585l4sSJ+Pn5sW3bNhYsWECdOnXw9PTk8OHD9OvXjzfffJMZM2bwzz//MHr0aPLkycOIESMYOXIkoaGh+Pn5UblyZb744guGDBnC0qVLadGiBQD//PMPGzZsYNmyZdy8eZO3336b9u3b07FjRz755BO2b9/OggULaN68Oc7OzmzcuBF/f3/8/PyoWbMmoaGhTJs2jfPnzzNmzJhM7aPJZCI2NjZT6xpBXFycxX/FOuLj461dgohIthQXF4fJZHqs2zSZTOnOPjkqsL766qu8+OKLAHTp0oVvv/2WESNG4O7uDoCXlxfh4eEAbNiwgWeeeQY/Pz/z+gsXLqRBgwZ89dVXtGrVipCQECZNmkTTpk0BmDlzJnv37k2z71u3bvHKK6/w4osvUq5cOeDOBVoffPABJ06cMAfWxMREJk+eTPXq1YE7IXfw4MH8999/PPPMM5ne71deeQWA4cOH8/HHH3PgwAE8PT358MMPcXd3N18oVrVqVaZNm8a///5LREQEu3fvJigoiObNmwMwZMgQTpw4QVBQkDmwJiYmMnHiRJydnQFo2LAhhw4dYsyYMdjY2DBgwACWLl3KX3/9hbOzM8uWLWPAgAG8/PLLAFSoUIEbN24wZcoU3nnnHezt7TO8j4mJiYSFhWXq+BhJdHS0tUvI1VI+WRARkYyJiorKkpMudnZ26WqXowJr5cqVzT+nzPO8+2yovb09CQkJAISGhhIREYGHh+XXjcXHxxMREUFUVBSJiYm4ublZrJ8SNO+VP39+evTowVdffcXatWs5deoUx48f599//031UXjVqlXNPxcuXBi4E8gy6+7tARQqVMi8vRMnTtCoUSOL5S1btgRgx44dANSpU8diecr0h7vdfWwdHBwoX768+a+ilAAaHx/PpUuXOHfuHIsWLWLJkiXmdZKTk4mPjycmJiZVvelha2uLk5NThtcziri4OKKjo3F0dNT32FvRhQsXrF2CiEi2VLly5cd+hvXkyZPpbpujAmu+fKl3536nmpOTk2nQoAGTJk1Ktaxw4cKcPXs23X3AnUDSvXt34uLiaN26Na+88goTJ06ke/fuqdqm9dfEo/wSPGh7+fLly/BUg+Tk5FT7aWtra/E4T560pz+nhPNx48alCsoAZcqUyVAtKWxsbChQoECm1jUSBweHHLEf2VVmzu6LiAhZcrIlI/kkx1x0lVHVqlUjIiKCMmXKUKlSJSpVqkTRokWZOXMm4eHhVK1aFXt7ew4cOGBe5/bt2xw/fjzN7f34448cO3aMjz76iGHDhtGmTRsKFSrExYsXH/tfJBlRtWpVjh49avHcmjVrePXVV80f8d+9jwD79+/P9NnM4sWLU7x4cU6fPm0+rpUqVeLYsWMsXLgwU9sUERGR3C3XBtZu3bpx/fp1Ro4cSVhYGMePH2fUqFEcOXKEatWqUaBAAXr06MHixYvZuXMnERERTJo0ifPnz6e5vdKlSwPw2WefcfbsWfbv38+gQYNITEw0T0Owhn79+nHo0CEWLlxIVFQU33//PcHBwbRo0QInJyeaNm3KlClT2LNnD1FRUSxZsoTdu3fj4+OTqf5sbGzo168fH330ER999BGnT59m165dTJkyBTs7u3TPVRERERFJkaOmBGREhQoVWLduHf7+/nTr1o28efNSq1Yt1q5dS/HixQEYNWoU9vb2TJ06lZs3b9K6dWu8vb3T3J67uzvjxo1jzZo1LFy4kFKlStGmTRvKlCnD4cOHn+SuWahevTrLli1j8eLFrFixgpIlS9KzZ08GDhwIQEBAAAsWLGDChAlcu3aNatWqERgYaJ7nmhk+Pj7Y29vz0UcfMWfOHIoXL07Hjh0ZMWLE49otERERyUVsTNb8vFokHVKmNNx9AVx2ExsbS1hYGNWrV9ccVis6f/48pUuXpuWcLwm/pDsGiIg8zHNli/DFsKz5hs2MvL/n2ikBIiIiIpI95NopAUZz/vx5XnrppQe2cXV1Zf369U+oIhERERFjUGA1iBIlSrB169YHttEteURERCQ3UmA1iLx581KpUiVrlyGSK1QpURDb/NauQkTE+JyeKWTtEgAFVhHJheZ2qkmRIkWsXYaISLaQlGwib56MfQnR46aLrkQk10i5KUpWfB+2pE9cXByhoaEaAyvSGBhDdhoHa4dVUGAVkVxId/OzHpPJRFxcnMbAijQGxqBxyBgFVhERERExNAVWERERETE0BVYRERERMTQFVhERERExNAVWERERETE0BVYRERERMTQFVhERERExNAVWERERETE0BVYRERERMTQFVhERERExNAVWERERETE0BVYRERERMTQFVhERERExNAVWERERETE0BVYRERERMTQFVhERERExNAVWERERETE0BVYRERERMTQFVhERERExNAVWEcl1bGxsrF2CiIhkgAKriOQaKUHVwcHBypVkL0nJJmuXICK5XD5rFyAi8qSN+fQwZ25Yu4rswemZQizq4mHtMkQkl1NgFZFcJ/LCTcIvJVm7DBERSSdNCRARERERQ1NgFRERERFDU2AVEREREUNTYM1B9u7di4uLCzExMdYuRUREROSxUWAVEREREUNTYBURERERQzNMYHVxcSEgIIDmzZvj5eVFZGQkt27dYuHChbRo0QI3Nzc6dOjArl27LNY7ePAgvXr1ok6dOtSvX5/33nuPq1evmpd7e3vz0UcfMXToUGrWrMnzzz/Ppk2bOHjwIB06dKBmzZp06dKF06dPm9fZunUrbdu2xc3NjSZNmjBjxgwSEhLSvS/e3t4EBQUxYMAA3N3dadmyJZs2bbJo88cff9C9e3fc3d1p1qwZU6ZM4caNGxbbmDlzJm3atKF+/fr89ttv6e7/+++/p127dtSoUYO2bdvy3XffmZclJSWxZs0aXnzxRdzc3HjxxRf55JNPzMvTmlYQExODi4sLe/fuBeDixYsMGzaM+vXr4+7uTpcuXdi3b5+5fUJCAvPmzaNJkyZ4eHjQuXNnfvrpp3TXLyIiInI3Q92HdePGjSxfvpykpCSqVKnCoEGDCA0Nxc/Pj8qVK/PFF18wZMgQli5dSosWLThy5Ag9e/akc+fO+Pn5cfHiRaZNm4aPjw+bNm0iT547edzf35/33nuPMWPGsHz5ciZPnkzVqlV57733KFiwIMOHD2f+/PksXryY48ePM2HCBObPn4+7uzsRERGMGjWKp59+mkGDBqV7X5YuXcqAAQMYO3YsP/zwA35+fhQsWJA2bdpw/PhxevfuzcCBA5kxYwYXLlxg7ty5+Pj4sHHjRvO38WzYsIHg4GAKFy6Mi4tLuvv+8MMPmTp1Ks888wzz589n+PDh/PzzzxQsWJDZs2ezbds2Jk6ciJubGz///DNTp04lPj6enj17pmv7kydPJj4+nnXr1mFnZ0dQUBCDBg3ihx9+oECBAowbN46//vqLefPmUbp0afbs2cPAgQNZsmQJzZo1S/d+3M1kMhEbG5updY0gLi7O4r9iHfHx8dYuIduKi4vDZHr0b7zSa8H6NAbGoHG4896e3q/KNlRgfeWVV3BzcwMgIiKC3bt3ExQURPPmzQEYMmQIJ06cICgoiBYtWrBq1SpcXFzw8/MDwMnJCX9/f9q3b8+PP/5I06ZNAXj++efp3LkzAL169WLjxo307NmTBg0aANC6dWvzmduYmBhsbGwoX748ZcuWpWzZsqxcuZJChQplaF+8vLwYMmQIAFWqVOHw4cOsXbuWNm3asHLlSho2bGgOwI6Ojvj7+/PCCy+wb98+6tevD0DTpk1p1KhRho/je++9Z97G4MGD2bVrFxEREVSpUoUNGzbg6+tLu3btzH2fOXOGoKAgevToka7tnz59GmdnZypWrIi9vT3jx4+nXbt25M2bl1OnTvH555/z6aefmseyT58+HD9+nJUrV2Y6sCYmJhIWFpapdY0kOjra2iXkaklJ+rKAzIqKinqsb6x6LVifxsAYcvs42NnZpaudoQJrpUqVzD+fOHECgDp16li08fT0xN/fH4Dw8HC8vLwslru4uFCkSBFOnDhhDqyVK1c2L8+fPz8A5cuXNz9nb29v/sg/5WPs1157DUdHRxo1akSLFi2oUaNGhvYlJTCmqFWrlvmj+dDQUE6dOoWHR+qvO4yIiDCve/fxyIi797dIkSIA3Lp1i8jISBITE9M8pqtXr+bixYvp2v6QIUN49913+eabb/D09KRx48a0adMGe3t7QkNDgTt/GNwtMTHRXEtm2Nra4uTklOn1rS0uLo7o6GgcHR31PfZWdOHCBWuXkG1Vrlz5sZ1h1WvBujQGxqBxgJMnT6a7raECa0qYfJDk5GTy5btT9v1OJScnJ2Nra2t+nNL+binTBe5lb2/Phx9+SGhoKD/99BM//fQT//vf/+jQoQOzZs1K766k6tNkMpn7TE5Opl27dgwcODDVesWKFTP/nJ7jkZa09s1kMpnfbO49ZsnJyalqvvuN6fbt2xbtW7ZsyY8//siPP/7IL7/8wooVK1i0aBGffPKJeb3169dTsGDBh9aVXjY2NhQoUCDT6xuFg4NDjtiP7Mre3t7aJWRbj/sNVa8F69MYGENuHof0TgcAA110dS9nZ2cADhw4YPH8/v37zWfanJ2d2b9/v8Xy48ePc+PGDapWrZqpfr///nuWLFmCq6srb731Fh9++CHDhg1jx44dGdrO0aNHLR7/8ccfuLq6AlCtWjX++usvKlWqZP6XlJTErFmz+OeffzJVd3pUqVKFfPnypTpm+/fvp2TJkhQtWtQc9O++AOzUqVPmnxMSEpg1axZnzpyhTZs2TJ8+nW+++YY8efLw3XffUa1aNQD+/fdfi/0LCQlh8+bNWbZvIiIiknMZNrA6OTnRtGlTpkyZwp49e4iKimLJkiXs3r0bHx8fAHr37s3x48eZOnUqERER7Nu3j9GjR+Pq6krDhg0z1W++fPlYunQpa9as4cyZMxw9epQ9e/ak+fH9g3zxxResW7eO6OhoVqxYwTfffEO/fv0A8PHxISwsDD8/P06ePMnhw4cZPXo0UVFRODo6Zqru9ChcuDCdO3dm8eLFbN++nVOnTrF+/Xo+/vhjfHx8sLGxwdnZmYIFC/L+++9z6tQpfv/9dwICAsx/BdnZ2XH48GEmTpzIoUOHiImJISQkhJs3b+Lh4UG1atVo3rw5kyZNYvfu3Zw5c4aVK1cSHBxMhQoVsmzfREREJOcy1JSAewUEBLBgwQImTJjAtWvXqFatGoGBgbRs2RIADw8Pli9fzqJFi+jQoQOFChXihRdeYNSoURZTAjLCy8uLGTNmsGrVKgICAsifPz9NmzbF19c3Q9vp0KEDO3fuZM6cOTg6OrJw4ULznNpatWqZP0bv2LEjDg4ONGjQgLFjx6Z78nFmjR8/nqeffhp/f38uXLhApUqV8PPzM1+UVqhQIebPn4+/vz9t27alcuXKjBs3zhy2ARYtWsSsWbN4++23uX79OlWqVMHf3x9PT0/gzrgFBAQwadIkrl69SoUKFZg2bRqvvfZalu6biIiI5Ew2pscxi14seHt78+qrrzJ06FBrl5IjpEyvSLnrQHYUGxtLWFgY1atXz7VzlYzg/PnzlC5dmpZzviT8ku4YkB7PlS3CF8OaPLbt6bVgfRoDY9A4ZOz93bBTAkREREREwOBTAoxm6tSpbNmy5YFtFi1alCV9e3p6PvAekk8//TTffvttlvQtIiIiYk0KrBkwZMgQ3nzzzQe2eeaZZ7IkOIaEhDzwHoiPcssoERERESNTYM2AYsWKWdwn9UmqWLGiVfoVyYmqlCiIbeZuc5zrOD2TsW/5ExHJCgqsIpLrzO1U85G+eS23SUo2kTdP+m/wLSLyuOlzZBHJNVKm1cTFxVm5kuxFYVVErE2BVURyHd3NT0Qke1FgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVkVzHxsbG2iWIiEgGKLCKSK6RElQdHBysXIkxJCWbrF2CiEi65LN2ASIiT9qYTw9z5oa1q7Aup2cKsaiLh7XLEBFJFwVWEcl1Ii/cJPxSkrXLEBGRdNKUABERERExNAVWERERETE0BVYRERERMTQF1kz6+++/+eKLL9Ldfs+ePZw8eTLd7Xv27Imvr29mShMRERHJURRYM2ns2LH8+OOP6Wp79uxZBg4cyMWLF7O4KhEREZGcR4H1CTCZdK9DERERkcxSYM2Enj17sm/fPrZs2YK3tze3bt1i4cKFtGjRAjc3Nzp06MCuXbsAiImJoUWLFgD06tWLwMBAAL799lu6dOmCh4cHbm5udOrUiV9++eWRanrvvfd4/fXX8fT0ZOvWrQBs3bqV9u3b4+7ujre3N0FBQSQnJ5vX++effxg9ejReXl7UqlWLvn37cuLECfNyX19fxo0bR0BAAPXr16dOnTpMmzaNc+fOMXDgQGrWrEmrVq34/vvvzescOXKEbt264eHhQd26dRk6dCh///13pvdNREREcjfdhzUTAgMDGThwIKVLl8bPz4+RI0cSGhqKn58flStX5osvvmDIkCEsXbqUZs2asWnTJl5//XUCAwPx8vLizz//ZPDgwbz77rvMmzePmzdvEhAQwOjRo/nuu++ws7PLVF0hISHMmzePZ599lhIlSrBmzRr8/f3x9fXFy8uLo0ePMnXqVK5cuYKvry83btyga9euVKhQgffffx87OzuWLl1Kjx492LZtG2XLlgVg+/btdO7cmU8++YTff/+d8ePH8/XXXzN69GjzPvj6+vLLL79gMpkYMGAAnTt3Zs6cOVy7dg0/Pz/ee+891qxZk+ljbjKZiI2NzfT61hYXF2fxX7GO+Ph4a5dgOHFxcU/0UyC9FqxPY2AMGoc77+3p/apsBdZMeOqpp7C1tSV//vxcvnyZ3bt3ExQURPPmzQEYMmQIJ06cICgoiBYtWlCsWDEAihYtSsGCBcmbNy8TJkyge/fu5m326tULHx8fLl68SJkyZTJVV/Xq1WnXrh1w55dg+fLl9OjRw9yPo6MjV65cYc6cOQwePJjt27dz+fJlQkJCzDXOnz+fF154gfXr1/Puu+8CUKRIEcaPH0/evHmpVKkS/v7+NGjQgA4dOgDQrVs39uzZw4ULF7Czs+Py5cs888wzlC9fHhsbGxYuXPjI83cTExMJCwt7pG0YQXR0tLVLyNWSkvRlAfeKioqyyhumXgvWpzEwhtw+Duk9SafA+ohSPj6vU6eOxfOenp74+/unuU716tUpWrQoy5cvJyoqiujoaHMYe5Q31EqVKpl/vnTpEhcuXEhVV926dUlMTCQyMpLw8HAcHR3NYRXA3t4ed3d3i2kBFStWJG/evObHDg4OVKhQwWIduHP2qmTJkvTr149p06axZMkSGjVqxPPPP8+LL76Y6f0CsLW1xcnJ6ZG2YU1xcXFER0fj6Oio77G3ogsXLli7BMOpXLnyEz/DqteCdWkMjEHjQIbunqTAmkWSk5PJly/tw/v777/j4+ND06ZN8fT0pG3btsTFxTF48OBH6jN//vzmn+/3BpQSiPPly3ffU/FJSUkWtdva2qZqkyfP/ac/jx49mm7duvH999/z66+/MnnyZIKDg9m6dWumpzvY2NhQoECBTK1rJA4ODjliP7KrlD+u5P9Y641SrwXr0xgYQ24eh/ROBwBddPXInJ2dAThw4IDF8/v37zefEbx3QFauXEn9+vVZsmQJvXv3xsvLi3/++Qd4fHcUKF68OMWLF0+zLltbWypWrIizszNRUVEWH9fHx8fz559/ZvpsZmRkJJMmTaJ48eJ07dqVxYsXs2LFCiIiIjh+/Pgj7ZOIiIjkTjrDmkkFCxbk7NmzFCpUiKZNmzJlyhTgzjzRL774gt27d7Nw4UIA819O4eHhuLq6UqZMGXbt2sX+/fspXbo0e/fuZdGiRQAkJCQ8lvpsbGzw8fFh0aJFlC9fnsaNG3PkyBGWLFnCG2+8QeHChWnXrh1BQUEMHz6cd999Fzs7O5YtW0ZsbCxvvPFGpvp96qmn+Pzzz7l16xZvvfUWefLkYfPmzRQtWpQqVao8ln0TERGR3EWBNZO6dOnC2LFjad++vTmcTpgwgWvXrlGtWjUCAwNp2bIlAE8//TSvvfYac+fO5dSpUwwbNowLFy4wcOBAAJycnJg5cybvvvsuR44coWrVqo+lxn79+mFnZ8fatWuZNWsWpUuXpn///vTt2xe4czHVunXrmDNnDr179wbuzMXdsGGDxRzVjChWrBgrVqzA39+fzp07k5SURK1atVi9ejWFChV6LPslIiIiuYuNSXe1F4M7evQoAG5ublauJPNiY2MJCwujevXquXaukhGcP3+e0qVL03LOl4Rfyt13DHiubBG+GNbkifer14L1aQyMQeOQsfd3zWEVEREREUPTlACDW758OcuWLXtgG19f30zPORURERExOgVWg+vcuTOtWrV6YJu776MqIiIiktMosBpc0aJFKVq0qLXLEMlRqpQoiG3+h7fLyZye0UWQIpJ9KLCKSK4zt1NNihQpYu0yrC4p2UTePOm/cbeIiLXooisRyTVSbooSFxdn5UqMQWFVRLILBVYRyXV0Nz8RkexFgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVURyHRsbG2uXICIiGaDAKiK5RkpQdXBwsHIlj0dSssnaJYiIPBH5rF2AiMiTNubTw5y5Ye0qHo3TM4VY1MXD2mWIiDwRCqwikutEXrhJ+KUka5chIiLppCkBIiIiImJoCqwiIiIiYmgKrCIiIiJiaBkKrC4uLoSEhGRVLeni6+tLz549rVpDVgkMDMTb29v82AjHW0RERMTast1FV+PHjycpKXdcLPHTTz9RuHBha5chIiIiYlXZLrDmpgBXsmRJa5cgIiIiYnUZnsMaFRVFnz59cHd3p3HjxgQHB1ss/+677+jcuTMeHh40btyY2bNnEx8fb16e1sfc3t7eBAYGApCUlMS8efNo2rQpNWrU4KWXXmLDhg3mtndPCdi7dy8uLi58//33vPzyy9SoUYO2bduyZ88ec/ukpCQCAgJo3LgxNWvWZOjQocyYMSND0wp69uzJwoULmThxIh4eHjRo0IBly5YRGRlJ9+7dcXd3p3379hw5csS8zvXr15k4cSINGjSgTp069OrVi6NHj1psd+PGjbRs2RJ3d3cGDRrE1atXLZbffawSEhLw9/fnhRdeoEaNGtSvX5+RI0dy+fJlAGJiYnBxceHLL7/k9ddfx83NjRYtWvDpp5+mez8BDhw4QJ8+fahTpw41atTg5Zdf5vPPP7dos3btWry9vXF3d6d3794sWbLEYirD+fPnGTFiBJ6entSvX5+BAwcSHR2doTpEREREUmT4DOu6deuYNGkSU6dOZfv27SxYsAB3d3caNmzIrl27GDp0KEOGDGH27NmcOnWKyZMnc/bsWXMgfZiPP/6Yr776ioCAAEqVKsWePXuYPHky1apVw9PTM8115s2bx/jx4ylevDgLFixg9OjR/PDDDxQsWJD58+ezZcsWpk6dStWqVfn444/56KOPqFu3bob2e8WKFQwaNIjPPvuMzz77jEWLFrFlyxZ8fX0pX748EyZMYPLkyYSEhGAymejfvz+2trYEBwdTqFAhtm3bRteuXfnkk09wdXXliy++YOrUqbz33ns0atSIb775hoCAAMqUKZNm/3PnzmX37t3Mnj2b8uXL89dffzF27Fjef/993nvvPXO72bNn4+fnh6OjI6tXr2bixInUr1+fChUqPHQfz58/j4+PD926dWPy5Mncvn2bFStWMG7cOBo0aECJEiVYv349CxYsYOLEidSpU4evvvqKxYsXm+uOjY2lZ8+ePPvss6xbt448efKwevVqOnfuzPbt2ylVqlSGjnsKk8lEbGxsptY1gri4OIv/inXc/cdzThEXF4fJlH2+8UqvBevTGBiDxuHOe3t6vyo7w4G1a9eudOjQAYBBgwaxatUq/vzzTxo2bEhwcDAtW7Zk8ODBAFSpUgWTycTbb79NREQEVatWfej2T58+TYECBahQoQIlS5akR48eVKlShcqVK993neHDh9OwYUPzz6+88grh4eE8++yzfPzxx4wbN45WrVoBMHHiRA4ePJjR3cbZ2ZlBgwYB4OPjw+LFi2nTpg0tWrQAoGPHjsycOROA3377jYMHD/Lrr79SrFgxAEaOHMkff/zBhx9+yOzZs/nwww9p06YN3bt3B+Ctt97i0KFDHD9+PM3+3dzcaNWqFfXq1QOgXLlyNG7cmBMnTli069Onj7mmsWPHsmnTJg4fPpyuwJqQkMCQIUPo27cvefLcOfk+YMAAQkJCiI6OpkSJEqxcuZJevXrRqVMnAN5++21CQ0M5duwYAF988QWXL1/G398fW1tbAGbMmMHevXv55JNPGDp0aHoOdyqJiYmEhYVlal0j0Zlm68qJ89+joqKy5RueXgvWpzEwhtw+DnZ2dulql+HAem9wLFKkiPmsRXh4OG3btrVYnnIm88SJE+kKrN27d2fXrl08//zz1KhRAy8vL1q3bk3x4sXvu06VKlXMPxcqVAi4E3AiIiK4desWtWrVsmhfp06d+wbD+7l7v1O+h/zuEGhvb09CQgKAObylBMcUCQkJDzxWHh4e963rlVde4ddff2XBggVER0cTERFBZGRkqrPOdx/jlPm+iYmJ6drHChUq8Nprr7Fu3TpOnjxJdHS0OSQmJSVx+fJlzp49m+bxTNnn0NBQbty4YQ7WKeLj44mIiEhXHWmxtbXFyckp0+tbW1xcHNHR0Tg6OuaY77HPji5cuGDtEh67ypUrZ7szrHotWJfGwBg0DnDy5Ml0t81wYM2bN2+q51L+Z5nWqd2UMxr58uVL1T7F3YHK0dGRnTt3sm/fPn7++Wd2795NUFAQs2bN4tVXX02zprTSuclkMvf5OP5nnnK28G4pZyHvlZycTKFChdK8JdXdtd5bV1p9pJg8eTI7duygQ4cONGvWjLfffpuVK1dy/vz5+27/fv3cT0REBF27dsXV1RUvLy9atGjB008/zeuvvw6QruOZnJxM5cqVef/991MtK1CgQLrqSIuNjc0jrW8UDg4OOWI/sit7e3trl/DYZdc3Or0WrE9jYAy5eRzSOx0AHvMXBzg7O3PgwAGL5/bv3w/835k/W1tbrl+/bl5+48YNLl26ZH784YcfsnPnTry8vBgzZgzbt2+nYcOG7NixI8P1VKpUifz583Po0CGL5+++OCorODs7c+PGDRISEqhUqZL53/Lly9m9ezcA1atXT3Ws7r0oK8Xly5fZsGEDkydP5r333qNjx45Ur16dyMjIx3pmZcOGDRQvXpw1a9bQv39/mjZtaj4jZTKZKFy4MOXKlXvg8XR2dubvv/+mcOHC5v0uV64c/v7+/P7774+tVhEREck9Hmtg7du3Lzt37mTp0qVERUWxZ88epk2bRvPmzc2B1cPDg40bN3Ls2DHCw8MZM2aMxdnXixcvMnXqVHbv3s3Zs2f54YcfCA0NxcPDI8P1ODg40LNnTxYvXsyuXbuIiopi/vz5qQLX49akSROqV6/O8OHD+fXXXzl16hRz5sxh8+bN5uPw1ltv8c0337BixQqio6P56KOP+Prrr9PcXuHChSlcuDC7d+/m1KlTnDhxgokTJ3Ls2DHzNITHoXTp0pw7d47vv/+es2fPsnPnTiZPngxg7qd///6sW7eOLVu2cOrUKdasWcOXX35p3kb79u0pWrQoQ4YM4dChQ0RERDBu3Di+//57qlWr9thqFRERkdzjsd6HtXXr1iQlJREcHMz7779PsWLFePnllxk2bJi5zeTJk5kyZQpdunShWLFi9OnTx+Lq7yFDhnD79m2mTZvGhQsXKFmyJN26dWPAgAGZqumdd94hMTGRCRMmEBcXR/PmzWnRokWWXi2cN29eVq1axbx58xgxYgRxcXFUrVqVwMBA88VhzZo1w9/fn8DAQBYtWkStWrXw8fFJdQspuPNR/KJFi5g9ezbt2rWjaNGi5ttaBQUFPbar53v16kVkZCRjxowhISEBR0dHRo4cyeLFizly5AjPP/88Xbt25erVqwQEBHD58mXq1avHq6++aj5bXLhwYdatW8fcuXPp168fSUlJVK9enZUrVyqwioiISKbYmLLTbP1M+Oabb6hTp475an24c5V/6dKlzVf1S/r98MMPVKtWzeL2WxMnTuT06dOsXbs2S/pMmSrh5uaWJdt/EmJjYwkLC6N69eq5dq6SEZw/f57SpUvTcs6XhF/K3ncMeK5sEb4Y1sTaZWSYXgvWpzEwBo1Dxt7fH+uUACNauXIlo0aNIiwsjDNnzrBmzRp+++032rdvb+3SsqVt27bx9ttvc+jQIc6ePcvWrVv57LPPeOWVV6xdmoiIiORQ2e6rWTNq/vz5zJ49m969e3Pr1i2cnJxYtGgRDRo0YPny5SxbtuyB6/v6+vLGG288oWqzTvv27Tlz5swD2/z8888P/Stv4sSJzJ49m8GDB3Pt2jUqVqxovhBMREREJCvk+MBavnx5lixZkuayzp07m79Q4H7unkqQnQUFBT30fqzpuT3OU089xezZsx9XWSIiIiIPleMD64MULVqUokWLWruMJ6Js2bLWLkHEMKqUKIhtfmtX8Wicnilk7RJERJ6YXB1YRSR3mtupJkWKFLF2GY8sKdlE3jzpv/G2iEh2leMvuhIRSZFyU5S4uDgrV/J4KKyKSG6hwCoiuU4Ov5ufiEiOo8AqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiuY6NjY21SxARkQxQYBWRXCMlqDo4OFi5EkhKNlm7BBGRbCOftQsQEXnSxnx6mDM3rNe/0zOFWNTFw3oFiIhkMwqsIpLrRF64SfilJGuXISIi6aQpASIiIiJiaAqsIiIiImJoCqwiIiIiYmgKrI/AxcWFkJAQa5fxQDExMbi4uLB3794s3Yavry89e/bMdB8iIiIi96PAKiIiIiKGpsAqIiIiIoamwPqIoqKi6NOnD+7u7jRu3Jjg4GCL5d999x2dO3fGw8ODxo0bM3v2bOLj483L05pW4O3tTWBgIABJSUnMmzePpk2bUqNGDV566SU2bNhg0X7z5s20bt0ad3d3Wrduzdq1a0lOTrZoc/jwYTp37kyNGjVo0aIFmzdvtli+detW2rdvj7u7O97e3gQFBaXaRgqTycSyZct4/vnnqVWrFuPHj7fYp5TttW3bFjc3N5o0acKMGTNISEhIxxEVERERsaT7sD6idevWMWnSJKZOncr27dtZsGAB7u7uNGzYkF27djF06FCGDBnC7NmzOXXqFJMnT+bs2bPmQPowH3/8MV999RUBAQGUKlWKPXv2MHnyZKpVq4anpycbN27E398fPz8/atasSWhoKNOmTeP8+fOMGTPGvJ01a9Ywffp0nJycWLVqFRMmTMDT05NKlSqxZs0a/P398fX1xcvLi6NHjzJ16lSuXLmCr69vqpo++OADVqxYwdSpU3F1dWXjxo18+umn1KtXD4Djx48zYcIE5s+fj7u7OxEREYwaNYqnn36aQYMGZeo4m0wmYmNjM7WuEcTFxVn8V6zj3j+srC0uLg6TKXd945VeC9anMTAGjcOd9/b0flW2Ausj6tq1Kx06dABg0KBBrFq1ij///JOGDRsSHBxMy5YtGTx4MABVqlTBZDLx9ttvExERQdWqVR+6/dOnT1OgQAEqVKhAyZIl6dGjB1WqVKFy5coALFu2jAEDBvDyyy8DUKFCBW7cuMGUKVN45513zNsZPHgw3t7eAIwYMYINGzZw7NgxKlasyPLly+nRowfdu3cHwNHRkStXrjBnzhxz7SlMJhMfffQRvXr1Mvc5btw4iwuyYmJisLGxoXz58pQtW5ayZcuycuVKChUqlJlDDEBiYiJhYWGZXt8ooqOjrV1CrpaUZKwvC4iKisq1b1Z6LVifxsAYcvs42NnZpaudAusjSgmOKYoUKWI+ixMeHk7btm0tltetWxeAEydOpCuwdu/enV27dvH8889To0YNvLy8aN26NcWLF+fSpUucO3eORYsWsWTJEvM6ycnJxMfHExMTg729PXAnLKcoWrQocOds06VLl7hw4QJ16tRJVWdiYiKRkZEUL17c/Pzly5f577//cHNzs2hfq1YtIiIiAGjSpAkeHh689tprODo60qhRI1q0aEGNGjUeur/3Y2tri5OTU6bXt7a4uDiio6NxdHQ0xPfY51YXLlywdgkWKleunCvPsOq1YF0aA2PQOMDJkyfT3VaB9RHlzZs31XMpb0BpnepOOcOTL1++VO1TJCYmmn92dHRk586d7Nu3j59//pndu3cTFBTErFmzaNKkCXDnDGejRo1S1VGmTBn+/fdfAPLkST1d2WQy3ffNMq0609rHFHe3s7e358MPPyQ0NJSffvqJn376if/973906NCBWbNmpbm9h7GxsaFAgQKZWtdIHBwccsR+ZFcpf8AZRW59kwK9FoxAY2AMuXkc0jsdAHTRVZZydnbmwIEDFs/t378fwHx21dbWluvXr5uX37hxg0uXLpkff/jhh+zcuRMvLy/GjBnD9u3badiwITt27KB48eIUL16c06dPU6lSJfO/Y8eOsXDhwnTVmLKNtOq0tbWlYsWKFs8XK1aMMmXKpGr/559/mn/+/vvvWbJkCa6urrz11lt8+OGHDBs2jB07dqSrJhEREZG76QxrFurbty8jRoxg6dKltGnThujoaKZNm0bz5s3NgdXDw4ONGzdSt25dbG1tWbhwocXZyosXL7J06VLy58/Ps88+S0REBKGhobz55pvY2NjQr18/FixYQNmyZWnatCnh4eFMmTKFZs2apWteiI2NDT4+PixatIjy5cvTuHFjjhw5wpIlS3jjjTcoXLgwV69etVinf//+zJkzhypVquDp6cm2bds4cuSIeVpBvnz5WLp0KYUKFaJFixZcuXKFPXv24OHh8RiProiIiOQWCqxZqHXr1iQlJREcHMz7779PsWLFePnllxk2bJi5zeTJk5kyZQpdunShWLFi9OnTx+Jq+CFDhnD79m2mTZvGhQsXKFmyJN26dWPAgAEA+Pj4YG9vz0cffcScOXMoXrw4HTt2ZMSIEemus1+/ftjZ2bF27VpmzZpF6dKl6d+/P3379k2zfffu3UlOTub999/nwoULNGnShE6dOhEVFQWAl5cXM2bMYNWqVQQEBJA/f36aNm2a5h0HRERERB7GxpTbZvxLtnP06FGAVBd6ZSexsbGEhYVRvXr1XDtXyQjOnz9P6dKlaTnnS8IvWe+OAc+VLcIXw5pYrX9r0mvB+jQGxqBxyNj7u+awioiIiIihKbCKiIiIiKEpsIqIiIiIoSmwioiIiIih6S4BIpLrVClRENv81uvf6ZnMf02xiEhupMAqIrnO3E41KVKkiFVrSEo2kTdP+r/lRUQkN9OUABHJNVLu4hcXF2flSlBYFRHJAAVWEcl1dPtpEZHsRYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRHIdGxsba5cgIiIZoMAqIrlGSlB1cHDI0n6Skk1Zun0Rkdwmn7ULEBF50sZ8epgzN7Jm207PFGJRF4+s2biISC6lwCoiuU7khZuEX0qydhkiIpJOmhIgIiIiIoamwCoiIiIihqbAKiIiIiKGluMC699//80XX3yR7vZ79uzh5MmT6W7fs2dPfH19s6y9tbcrIiIiYjQ5LrCOHTuWH3/8MV1tz549y8CBA7l48WIWVyUiIiIimZXjAmtGmEy6V6KIiIiI0eWowNqzZ0/27dvHli1b8Pb25tatWyxcuJAWLVrg5uZGhw4d2LVrFwAxMTG0aNECgF69ehEYGAjAt99+S5cuXfDw8MDNzY1OnTrxyy+/PFJdsbGxjBo1ilq1atGkSRPWrFljEZYjIiLo378/Hh4eNG7cmFGjRvHff/+ZlyckJDBz5kwaNmyIp6cn/v7+JCcnm5fv3bsXFxcXli9fTv369Xn11VdJSkrin3/+YfTo0Xh5eVGrVi369u3LiRMnLGrbunUr7du3x93dHW9vb4KCgszbjomJwcXFhe+//56OHTvi5uZGu3btOHToEJs2baJ58+bUrl2bUaNGER8fD0BSUhLz5s2jadOm1KhRg5deeokNGzY80vETERGR3C1H3Yc1MDCQgQMHUrp0afz8/Bg5ciShoaH4+flRuXJlvvjiC4YMGcLSpUtp1qwZmzZt4vXXXycwMBAvLy/+/PNPBg8ezLvvvsu8efO4efMmAQEBjB49mu+++w47O7tM1fX111/Ts2dPNm/ezLFjx5g0aRIAvXv35vz583Tr1o22bdvi6+tLXFwcgYGBdOnShe3bt1OgQAGmT5/Ot99+y+zZsylbtixBQUHs37+fChUqWPTz3XffsXHjRuLi4oiLi6Nr165UqFCB999/Hzs7O5YuXUqPHj3Ytm0bZcuWZc2aNfj7++Pr64uXlxdHjx5l6tSpXLlyxWJ+7NSpU5k2bRqlSpXC19eXt956ixo1ahAUFMSpU6cYOXIkHh4e9OjRg48//pivvvqKgIAASpUqxZ49e5g8eTLVqlXD09Mz02NrMpmIjY3N9PrWFhcXZ/FfsY6UP6yehLi4OH2Kkwa9FqxPY2AMGoc77+3p/arsHBVYn3rqKWxtbcmfPz+XL19m9+7dBAUF0bx5cwCGDBnCiRMnCAoKokWLFhQrVgyAokWLUrBgQfLmzcuECRPo3r27eZu9evXCx8eHixcvUqZMmUzV5erqyoQJEwCoWrUqERERrFq1it69e7NhwwaeeeYZ/Pz8zO0XLlxIgwYN+Oqrr2jVqhUhISFMmjSJpk2bAjBz5kz27t2bqh8fHx8cHR0B+Pjjj7l8+TIhISHm/Zw/fz4vvPAC69evZ/To0SxfvpwePXqY99fR0ZErV64wZ84cBg8ebN5unz59aNSoEQAdOnRg6tSpTJo0iUqVKuHi4oKrqyvh4eEAnD59mgIFClChQgVKlixJjx49qFKlCpUrV87UsUuRmJhIWFjYI23DCKKjo61dQq6WlPTkviwgKioqV78RPYxeC9anMTCG3D4O6T0ZmKMC691SPvquU6eOxfMpH6mnpXr16hQtWpTly5cTFRVFdHS0OSQ9yhvdvTW4u7sTFBTEtWvXCA0NJSIiAg8Py69yjI+PJyIigqioKBITE3FzczMvs7e3p3r16qn6SQmrAOHh4Tg6OprDasp67u7unDhxgkuXLnHhwoVUtdWtW5fExEQiIyMpXrw4gEXYTPkO9rvP7trb25OQkABA9+7d2bVrF88//zw1atTAy8uL1q1bm7eVWba2tjg5OT3SNqwpLi6O6OhoHB0ds/x77OX+Lly48MT6qly5ss6wpkGvBevTGBiDxoEM3aUpxwbW+0lOTiZfvrR3+/fff8fHx4emTZvi6elJ27ZtiYuLszjbmBl58lhOFU5OTsbGxgZbW1uSk5Np0KCBeZrA3QoXLszZs2fT3GZa+2Bvb2/++X6n2ZOSksiXL99930hTgvnd20+rr3v3KYWjoyM7d+5k3759/Pzzz+az3LNmzeLVV19Nc530sLGxoUCBAple3ygcHBxyxH5kV3e/RrJabn0DSi+9FqxPY2AMuXkc0jsdAHLYRVd3c3Z2BuDAgQMWz+/fv998pu7eA7Vy5Urq16/PkiVL6N27N15eXvzzzz/Ao91R4NixYxaPDxw4QPny5XFwcKBatWpERERQpkwZKlWqRKVKlShatCgzZ84kPDycqlWrYm9vb7Eft2/f5vjx4w/s09nZmaioKItbdsXHx/Pnn3/i5ORE8eLFKV68eJrHx9bWlooVK2ZqXz/88EN27tyJl5cXY8aMYfv27TRs2JAdO3ZkansiIiIiOS6wFixYkLNnz1KoUCGaNm3KlClT2LNnD1FRUSxZsoTdu3fj4+MDYP6LJjw8nOvXr1OmTBlOnDjB/v37iYmJYfPmzSxatAjA/JF3Zvzxxx/MmzePiIgINm3axMcff8ygQYMA6NatG9evX2fkyJGEhYVx/PhxRo0axZEjR6hWrRoFChSgR48eLF68mJ07dxIREcGkSZM4f/78A/ts164dRYoUYfjw4Rw5coTjx4/z7rvvEhsbyxtvvIGNjQ0+Pj6sW7eO9evXc+rUKbZv386SJUt44403KFy4cKb29eLFi0ydOpXdu3dz9uxZfvjhB0JDQ1NNeRARERFJrxw3JaBLly6MHTuW9u3bs3v3bhYuXMiECRO4du0a1apVIzAwkJYtWwLw9NNP89prrzF37lxOnTrFsGHDuHDhAgMHDgTAycmJmTNn8u6773LkyBGqVq2aqZpef/11oqOjefXVVylWrBijRo2iY8eOwJ25oOvWrcPf359u3bqRN29eatWqxdq1a83zPkeNGoW9vT1Tp07l5s2btG7dGm9v7wf2WaRIEdatW8ecOXPo3bs3cGcu7YYNG8zzT/v164ednR1r165l1qxZlC5dmv79+9O3b99M7SfcubDt9u3bTJs2jQsXLlCyZEm6devGgAEDMr1NERERyd1sTLoqQAzu6NGjABYXnmU3sbGxhIWFUb169Vw7V8kIzp8/T+nSpWk550vCL2XNHQOeK1uEL4Y1yZJt5wR6LVifxsAYNA4Ze3/PcVMCRERERCRnyXFTAp6U5cuXs2zZsge28fX15Y033nhCFYmIiIjkTAqsmdS5c2datWr1wDZ33wNVRERERDJHgTWTihYtStGiRa1dhoiIiEiOp8AqIrlOlRIFsc2fNdt2eqZQ1mxYRCQXU2AVkVxnbqeaFClSJMu2n5RsIm+e9H+Di4iIPJjuEiAiuUbKXfzi4uKytB+FVRGRx0uBVURyHd1+WkQke1FgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFZFcw8bGxtoliIhIJiiwiki2Z0pKSlc7BwcHQMFVRCS7yWftAkREHpVN3rycHf0uCZGRD2wXX67cE6pIREQeJwVWEckREiIjuRUa+sA2iYmJT6gaERF5nDQlQEREREQMTYFVRERERAxNgVVEREREDE2BNRfw9vYmMDAw3e0vX77Mpk2bzI979uyJr69vVpQmIiIi8lC66EpSmTt3LjExMbz++usABAYGkjdvXitXJSIiIrmVAqukYjKZLB4/9dRT1ilEREREBE0JyFIuLi5s2LCBrl274u7uTrt27di9e7dFm++++47OnTvj4eFB48aNmT17NvHx8eneRmBgIN7e3hbbDAkJwcXF5b51bd68mQ4dOuDu7k6tWrXo2bMnx44dA8DX15ctW7awb98+8zbunRJw8OBBevXqRZ06dahfvz7vvfceV69eNS/39vbmgw8+YOjQoXh4eFC/fn1mzpzJ7du3M3EURUREJLfTGdYsNnfuXEaPHs2MGTMICQlhyJAhrF+/ntq1a7Nr1y6GDh3KkCFDmD17NqdOnWLy5MmcPXvWYs7pg7aRUd988w2TJk1i+vTp1K1blwsXLjB9+nTGjx/P1q1bGT9+PLdu3eLcuXNpzns9cuQIPXv2pHPnzvj5+XHx4kWmTZuGj48PmzZtIk+eO38DBQYG8u677zJq1Ch++uknpk+fjqurKx06dMjUcTSZTMTGxmZqXSOIi4uz+K88PjY2NuZvsEqv+Pj4bP37lJ3ptWB9GgNj0DjceW9P7zcPKrBmsddee43u3bsDMHr0aH7//XfWrVtH7dq1CQ4OpmXLlgwePBiAKlWqYDKZePvtt4mIiKBq1aoP3UZGPfXUU0yfPt0cHMuVK8frr7/OpEmTAChcuDD58+fH1taWkiVLplp/1apVuLi44OfnB4CTkxP+/v60b9+eH3/8kaZNmwLQpEkTevXqBYCjoyOffvopf/zxR6YDa2JiImFhYZla10iio6OtXUKO4+DggKura4bWOXfuHP/9918WVSTpodeC9WkMjCG3j4OdnV262imwZrF69epZPK5Zsya//PILAOHh4bRt29Zied26dQE4ceKEObA+aBsZVbduXYoVK8ayZcs4deoUUVFRhIWFkZycnK71w8PD8fLysnjOxcWFIkWKcOLECXNgTak9ReHChR/pW4ZsbW1xcnLK9PrWFhcXR3R0NI6Ojhk+GygPlt6/zu9WunRpSpQokQXVyMPotWB9GgNj0DjAyZMn091WgTWL5ctneYiTk5PNH5undSo8KSkp1XoP2kbKdu72oLmiX3zxBWPGjOHll1/G3d2dTp06ER4eztSpU9O1P/c7fZ+cnIytra35cVp/Md1bZ0bY2NhQoECBTK9vFA4ODjliP7I7e3t7jYOV6bVgfRoDY8jN45CREw666CqLHT161OLxoUOHeO655wBwdnbmwIEDFsv3798PWJ6hfNA2bG1tuXHjhkUYPHXq1H3rCQoKolOnTsyZM4fu3btTt25dzpw5A/xfoHzQL5Czs7O5xhTHjx/nxo0bqc6qioiIiDwOCqxZbO3atWzfvp2oqCjmzJnD8ePHefPNNwHo27cvO3fuZOnSpURFRbFnzx6mTZtG8+bNLcLfg7ZRu3Ztrl27xgcffEBMTAzbt28nJCTkvvWUKVOGP/74g2PHjnH69GnWrFnDunXrAEhISACgQIEC/Pvvv+Yge7fevXtz/Phxpk6dSkREBPv27WP06NG4urrSsGHDx3bcRERERFIosGaxN954g9WrV9O+fXv279/PypUrefbZZwFo3bo18+fP56uvvqJdu3ZMmjSJtm3bsnDhwnRvo169eowYMYJ169bRpk0btm7dytixY+9bz8SJEylRogQ9evTg9ddfZ8+ePcydOxeAw4cPA9ChQwfi4uJ4+eWX+ffffy3W9/DwYPny5fz555906NCBd955Bw8PD1avXm0xJUBERETkcbExPcrEQnkgFxcXZs2aRceOHa26jewuZUqEm5ublSvJvNjYWMLCwqhevXqunauU1aI6vsat0NAHtkmsVo2an2/n3LlzlCpV6glVJnfTa8H6NAbGoHHI2Pu7zrCKiIiIiKEpsIqIiIiIoem2VlnoxIkThtiGiIiISHamM6wiIiIiYmg6wyoiOYJdlSoPbWNTrtwTqERERB43BVYRyfZMSUmUmz/voe2uXbsGSwKfQEUiIvI4aUqAiGR7NnnzpqtdXFwc8GhfEywiIk+eAquI5BoKqiIi2ZMCq4iIiIgYmgKriIiIiBiaAquIiIiIGJoCq4iIiIgYmgKriIiIiBiaAquIiIiIGJoCq4iIiIgYmgKriIiIiBiaAquIiIiIGJoCq4iIiIgYmgKriIiIiBiaAquIiIiIGJoCq4iIiIgYmgKriIiIiBiaAquIiIiIGJoCq4iIiIgYmgKriIiIiBiaAquIiIiIGJoCq4jkGjY2NtYuQUREMkGBVUSyLVNSUobaOzg4AAquIiLZTT5rFyAiklk2efNydvS7JERGpqt9fLlyWVyRiIhkBQVWEcnWEiIjuRUamq62iYmJWVyNiIhkBU0JEBERERFDU2AVEREREUNTYBURERERQ8v1gdXFxYWQkBBrl/FYeHt7ExgYaO0yRERERB6rXB9YRURERMTYFFhFRERExNCsFlhdXFzYsGEDXbt2xd3dnXbt2rF7926LNnv27KFjx464u7vTsmVLFi5cSEJCgnn5lStXmDJlCk2bNsXd3Z2uXbuyf/9+8/LAwEC6du1KcHAwDRo0oG7duowbN44bN27ct66H9fkw6enz+vXrTJw4kQYNGlCnTh169erF0aNHLbbRpUsXRo4cSe3atZkyZUq6+//vv/8YOnQotWrVon79+syaNYuku26ufvDgQXr16kWdOnWoX78+7733HlevXjUvT2taQc+ePfH19TU/XrlyJS+88AI1atTA29ubpUuXYjKZzMsf9RiKiIiI3M2q92GdO3cuo0ePZsaMGYSEhDBkyBDWr19P7dq1+eGHH3jnnXcYN24cXl5enD59mmnTphEVFcWiRYtISkrCx8eHxMRE5syZQ8mSJVm3bh29e/dmw4YNuLm5AZiD4MqVK7lx4wbjx49n+PDhrFixIlU9D+szvR7Up8lkon///tja2hIcHEyhQoXYtm0bXbt25ZNPPsHV1RW4Eyzd3NzYtm2bReB8mE8//ZSxY8cyZswY9u7dy/jx46lWrRqdOnXiyJEj9OzZk86dO+Pn58fFixeZNm0aPj4+bNq0iTx5Hv73y7fffktQUBALFy6kcuXKHDp0iDFjxlC+fHleeeWVx3YM72UymYiNjc30+tYWFxdn8V95dDY2NuZvrsqo+Pj4bP37lJ3ptWB9GgNj0DjceW9P7zcPWjWwvvbaa3Tv3h2A0aNH8/vvv7Nu3Tpq165NUFAQnTp1omvXrgBUrFiRKVOm8OabbxITE0NERATHjh1j+/btODs7A+Dn58fhw4dZuXIlCxcuBO68qS1cuJBSpUqZ2/Tv35/IyEiqVKliUc/D+ixfvny69utBfZ4/f56DBw/y66+/UqxYMQBGjhzJH3/8wYcffsjs2bPN2xk2bBiFCxfO0DFt2bIlb775JgAVKlTgww8/5M8//6RTp06sWrUKFxcX/Pz8AHBycsLf35/27dvz448/0rRp04du//Tp09jb21O+fHnKli1L2bJleeaZZyhbtizw+I7hvRITEwkLC8vUukYSHR1t7RJyDAcHB/MfeBl17tw5/vvvv8dckWSEXgvWpzEwhtw+DnZ2dulqZ9XAWq9ePYvHNWvW5JdffgEgNDSUI0eOsGXLFvPylI+dIyIiCA8Pp3DhwuawCneCoqenJz/++KP5OUdHR3NwBPDw8AAgPDw8VWB9WJ/pDVsP6jMmJgaAFi1aWKyTkJBAfHy8+XHx4sUzHFYBKleubPG4aNGi5u2Gh4fj5eVlsdzFxYUiRYpw4sSJdAXW9u3bs3nzZlq1aoWLiwteXl60bNnSHFgf1zG8l62tLU5OTpla1wji4uKIjo7G0dEx02cFxVJ6/ypPS+nSpSlRosRjrEbSS68F69MYGIPGAU6ePJnutlYNrPnyWXafnJxs/lg6OTmZfv368eqrr6Zar2TJkpw4cSLNN6zk5GSL7dra2qZaDpA3b940131Qn+n1oD6Tk5MpVKhQmrfSuvuvjPz586e7v7ultV8pgfF+p96Tk5Mtar57PipYfp1lsWLF2LZtGwcPHuTnn3/mp59+YtWqVQwdOpQhQ4Y8tmN4LxsbGwoUKJDp9Y3CwcEhR+xHdmdvb69xsDK9FqxPY2AMuXkcMnLiwap3Cbj7QiOAQ4cO8dxzzwFQrVo1IiMjqVSpkvnf+fPnmTt3Ljdv3sTFxYVr164RHh5usY0DBw5YnImLiori+vXr5scHDx4EoHr16qnqeVif6fWgPp2dnblx4wYJCQkW/SxfvjzVRWePm7Ozs8VFaQDHjx/nxo0bVK1aFbgTtu+uPTk52XxWGGDbtm1s2LCBOnXqMGzYMD755BNef/11duzYATy+YygiIiKSwqqBde3atWzfvp2oqCjmzJnD8ePHzfMv+/fvz86dOwkMDCQqKopff/2VcePGce3aNUqWLImXlxcuLi6MGjWKvXv3EhERwZQpUwgPDzdvAyA2NpYxY8YQHh7Or7/+ytSpU2nTpk2aH00/rM/0elCfTZo0oXr16gwfPpxff/2VU6dOMWfOHDZv3mwOjVmld+/eHD9+nKlTpxIREcG+ffsYPXo0rq6uNGzYEIDatWuzY8cOfv/9d6Kiopg8ebJFgI2Pj2fOnDls3bqVmJgY9u/fz759+8zTHh7XMRQRERFJYdUpAW+88QarV6/mr7/+4tlnn2XlypU8++yzALz00ksEBAQQHBxMcHAwRYsWpXnz5rz77rt3Cs+Xj9WrVzNnzhyGDh1KQkICzz33HGvWrKFWrVrmPsqUKYOzszPdunUjX758tGvXjtGjR6dZz8P6TK8H9Zk3b15WrVrFvHnzGDFiBHFxcVStWpXAwEBzaMwqHh4eLF++nEWLFtGhQwcKFSrECy+8wKhRo8xTAkaMGMHVq1fp378/Dg4OvP7667Rp08Y8TaBz585cvXqVZcuW8c8//1C0aFFefPFF8/49rmMoIiIiksLGdO+ExSfExcWFWbNm0bFjxyzrIzAwkC1btvDtt99mWR9G6DOnS5k6knKrsuwoNjaWsLAwqlevnmvnKmWVqI6vcSs0NF1tE6tVo+bn2zl37pzFhZHy5Oi1YH0aA2PQOGTs/V3fdCUiIiIihmbVKQHZycGDB/Hx8XlgmxdeeIGKFSs+9r6nTp1qcZuotCxatIjnn3/+sfctIiIiYm1WC6wnTpzI8j6GDh3K0KFDH8u2XF1d2bp16wPbFChQgJIlSz62PlMMGTLE4kKytDzzzDOPtU8RERERo9AZ1nSyt7enUqVKVum7WLFi5m/FEhEREcltFFhFJFuzu+cb6x7Eply5LKxERESyigKriGRbpqQkys2fl+72165dgyWBWViRiIhkBd0lQESyLZs0vor4QeLi4oDUXz8sIiLGpsAqIrmGgqqISPakwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiuYaNjY21SxARkUxQYBURwzAlJWXp9h0cHAAFVxGR7CaftQsQEUlhkzcvZ0e/S0JkZJZsP75cuSzZroiIZC0FVhExlITISG6FhmbJthMTE7NkuyIikrU0JUBEREREDE2BVUREREQMTYFVRERERAxNgdWKXFxcCAkJsVqfiYmJrFmz5pG36e3tTWBg4CNvR0RERCQtCqy5zE8//USbNm0A+Pzzz5k1a5aVKxIRERF5MN0lIJcpWbKk+WeTyWTFSkRERETSR2dYDeS7776jc+fOeHh40LhxY2bPnk18fLx5uYuLC5988gl9+vTB3d2dJk2aEBwcbLGN7du307p1a9zc3OjUqRNr167FxcXFYhshISGEhIQwbtw483N79+4lMDAQb29vi+2FhIRYrH/9+nXGjh2Lp6cnDRs2THNKwR9//EH37t1xd3enWbNmTJkyhRs3bjyOQyQiIiK5kAKrQezatYu3336bpk2bsnnzZqZNm8aXX37J6NGjLdrNnTuXDh06sG3bNl577TUWLFjA/v37AdizZw9jx46lU6dOfPbZZ7z22mv4+/un2V+bNm147733gDvTBDw8PNJV5/Dhwzly5AhBQUGsWrWKPXv2cPbsWfPy48eP07t3b7y8vPjss8+YP38+x44dw8fHR2d0RUREJFM0JcAggoODadmyJYMHDwagSpUqmEwm3n77bSIiIqhatSoAr776Kq+88gpwJzx+/PHHHDhwAE9PT1auXMlLL71E3759AahcuTKnTp1i9erVqfrLnz8/hQsXBiynCTxIZGQkP/30E2vWrMHT0xMAf39/mjdvbm6zcuVKGjZsyKBBgwBwdHTE39+fF154gX379lG/fv3MHB5MJhOxsbGZWtcI4uLiLP4rqdnY2Ji/OjWrxcfHZ+vfp+xMrwXr0xgYg8bhznt7er8qW4HVIMLDw2nbtq3Fc3Xr1gXgxIkT5sCa8t8UhQoVMn97z7Fjx2jVqpXFck9PzzQDa2ZrBHBzczM/V6JECSpUqGB+HBoayqlTp9I8YxsREZHpwJqYmEhYWFim1jWS6Ohoa5dgWA4ODri6uj6Rvs6dO8d///33RPqStOm1YH0aA2PI7eNgZ2eXrnYKrAaR1l8ZSUlJAOTL93/DlNbApnzUni9fPpKTkx+5jrvdvn07VZt7+7i7vuTkZNq1a8fAgQNTrVesWLFM12Vra4uTk1Om17e2uLg4oqOjcXR0fGJnEbOb9P6V/TiULl2aEiVKPLH+5P/otWB9GgNj0DjAyZMn091WgdUgnJ2dOXDgAG+++ab5uZS5qfeeVb2fZ599lsOHD1s8d+/ju90bEGxtbblx44ZFeD516pR5ecrZrz/++INmzZoBcO3aNU6fPm1uU61aNf766y8qVapkfi4yMpK5c+cycuRI8zSEjLKxsaFAgQKZWtdIHBwccsR+ZHf29vYaByvTa8H6NAbGkJvHISMnKnTRlUH07duXnTt3snTpUqKiotizZw/Tpk2jefPm6Q6s/fv35+uvv2b16tWcOnWKLVu28NFHH923fcoL5M8//+TWrVvUrl2ba9eu8cEHHxATE8P27dstvtigYsWKvPTSS0ydOpVffvmF8PBwxowZQ0JCgrmNj48PYWFh+Pn5cfLkSQ4fPszo0aOJiorC0dExcwdHREREcjUFVoNo3bo18+fP56uvvqJdu3ZMmjSJtm3bsnDhwnRv4/nnn2fKlCmsX7+etm3bsnHjRrp06YKtrW2a7Rs0aEDNmjXp0qULe/bsoV69eowYMYJ169bRpk0btm7dytixYy3WmTNnDs2aNWPEiBF0794dJycnatSoYV5eq1YtVqxYQXh4OB07duStt96iQoUKrF69Ot3zVERERETuZmPSvYZyjH379lGiRAmqVKlifi4oKIhPP/2UXbt2WbGyR3P06FHA8mKv7CY2NpawsDCqV6+eaz/6Sa+ojq9xKzQ0S7adWK0aNT/fzrlz5yhVqlSW9CEPpteC9WkMjEHjkLH3d51hzUF+/vln+vbty2+//cbff//N7t27Wbt2rfk2WCIiIiLZkS66ykEGDx7MzZs3GTNmDJcuXaJMmTL07t2bfv36Wbs0ERERkUxTYM1B7OzsmDBhAhMmTLB2KSIiIiKPjaYEiIiIiIih6QyriBiK3V0XDT5uNuXKZdm2RUQk6yiwiohhmJKSKDd/XpZt/9q1a7AkMMu2LyIiWUNTAkTEMGzy5s3S7cfFxQGpv4JYRESMTYFVRHINBVURkexJgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEREREDE2BVUREREQMTYFVRERERAxNgVVEcg0bGxtrlyAiIpmgwCqSC5iSkqxdgiE4ODgACq4iItlNPmsXICJZzyZvXs6OfpeEyEhrl2JV8eXKWbsEERHJBAVWkVwiITKSW6Gh1i7DqhITE61dgoiIZIKmBIiIiIiIoSmwioiIiIihKbCKiIiIiKEpsGYzLi4uhISEpLv933//zRdffGF+7O3tTWBgIAAmk4ktW7Zw8eLFR6qpZ8+e+Pr6PtI2RERERO5HgTWHGzt2LD/++KP58aeffoqPjw8Av//+O76+vsTFxVmrPBEREZGH0l0CcplixYqZfzaZTFasRERERCR9dIY1GzOZTKxYsYLWrVtTo0YN6tSpw4ABAzhz5gxw56P6ffv2sWXLFry9vYH/mxKwd+9eevXqBUCLFi0ICQkhJCQEFxcXiz727t2Li4sLMTExACQkJDBz5kwaNmyIp6cn/v7+JCcnW6wTERFB//798fDwoHHjxowaNYr//vsvqw+HiIiI5FAKrNnY2rVrCQ4O5t133+Xrr79m2bJlREVFMXv2bAACAwPx8PCgdevWfPrppxbrenh4mOeybtq0iTZt2qSrz+nTp7Njxw5mz57Nhg0b+Pvvv9m/f795+fnz5+nWrRsVKlTg008/JSgoiBs3btClSxdiY2Mf056LiIhIbqIpAdlYxYoVmT17tvnsably5WjdurX5IqunnnoKW1tb8ufPbzEVAMDOzo6iRYsCd6YJ5M+f/6H93bhxg5CQECZNmkTTpk0BmDlzJnv37jW32bBhA8888wx+fn7m5xYuXEiDBg346quv6NixY6b21WQyZevAmzJP2BrzhW1sbMxfSSp3xMfHZ+vfp+zMmq8FuUNjYAwahzvv7en9qmwF1mzM29ubw4cPs3jxYk6dOkVERAR//fUXpUqVypL+oqKiSExMxM3Nzfycvb091atXNz8ODQ0lIiICDw8Pi3Xj4+OJiIjIdN+JiYmEhYVlen2jiI6OfuJ9Ojg44Orq+sT7NbJz585pmoqVWeO1IJY0BsaQ28fBzs4uXe0UWLOx5cuXExgYSMeOHalXrx49e/Zk9+7dFrexyoy7/+K5ffv2Q9vny/d/v0bJyck0aNCASZMmpWpXuHDhTNdka2uLk5NTpte3tri4OKKjo3F0dHziZzvT+9drblK6dGlKlChh7TJyJWu+FuQOjYExaBzg5MmT6W6rwJqNvf/++wwZMoS33nrL/NzKlSvTffX/vUHG1tYWgOvXr1OkSBEATp06ZV5etWpV7O3tOXDgAM8++yxwJ9AeP36c+vXrA1CtWjV27NhBmTJlzH81XblyhbFjx9KnTx8aNGiQqX21sbGhQIECmVrXSBwcHHLEfmR39vb2Ggcr02vB+jQGxpCbxyEjJ1R00VU2VqZMGX7++WdOnjxJZGQkAQEB7Ny5k4SEBHObggULcvbsWc6dO5dq/ZQXyPHjx7l58ya1atUiT548LFy4kDNnzvDdd9+xatUqi/Y9evRg8eLF7Ny5k4iICCZNmsT58+fNbbp168b169cZOXIkYWFhHD9+nFGjRnHkyBGqVauWhUdDREREcioF1mxs7ty53Lp1i9dee40ePXoQHh7OlClTuHjxovk2VF26dCE8PJz27duTlJRksb6zszNNmzZl+PDhbNy4kQoVKjB16lS+//57Wrduzfvvv897771nsc6oUaPo1q0bU6dOpVOnTphMJvNFXwAVKlRg3bp1xMXF0a1bN3r06IGNjQ1r166lePHiWX9QREREJMexMenu8WJwR48eBbC42Cu7iY2NJSwsjOrVq1vto5+ojq9xKzTUKn0bRWK1atT8fDvnzp3LsosT5cGM8FrI7TQGxqBxyNj7u86wioiIiIihKbCKiIiIiKEpsIqIiIiIoSmwioiIiIih6T6sIrmEXZUq1i7B6mzKlbN2CSIikgkKrCK5gCkpiXLz51m7DKu7du0aLAm0dhkiIpJBmhIgkgvY5M1r7RIMIS4uDiDd3wYnIiLGoMAqIrmGgqqISPakwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCoiIiIihqbAKiIiIiKGpsAqIiIiIoamwCryBNjY2ODg4ICNjY21S8nVdPxFRLKnfNYuQMSaTElJ2OTNm+X9ODg44OrqmuX9yIM5ODgACq4iItmNAqvkajZ583J29LskREZauxR5AuLLlbN2CSIikgkKrJLrJURGcis01NplyBOQmJho7RJERCQTNIdVRERERAxNgVVEREREDE2BVUREREQMLUcFVhcXF0JCQrLNdrOr2NhY1q9fb37s6+tLz549rViRiIiI5GS66EoybNWqVYSEhNC9e3cAxo8fT1JSkpWrEhERkZxKgVUyzGQyWTwuXLiwlSoRERGR3CBHTQkAiIyMpGvXrri5ufHyyy/z888/Wyzfs2cPHTt2xN3dnZYtW7Jw4UISEhLMy8+dO8fbb7+Nh4cHzZo144svvrBYPzAwkC5dujBy5Ehq167NlClTADh48CC9evWiTp061K9fn/fee4+rV6+a17t16xYLFy6kRYsWuLm50aFDB3bt2mVeHhISQsuWLdmxYwfe3t64u7vTt29fzp8/z4wZM6hbty6NGjUiODjYvM7FixcZNmwY9evXx93dnS5durBv3750H6uQkBC8vb2ZMWMGnp6eDBw4EIBvv/2WLl264OHhgZubG506deKXX34x7/+SJUs4e/YsLi4uxMTEpJoSEBERwcCBA6lfvz516tRh2LBh/P333+muS0RERORuOS6wrl27lldeeYXPPvuMF154gb59+/Lnn38C8MMPP/DOO+/w+uuv8/nnnzNp0iS+/PJL3n33XQBu375Nv379uHz5MuvWrSMgIIDly5en6uPgwYMUL16cbdu28eabb3LkyBF69uyJk5MTGzduZPHixRw5cgQfHx+Sk5MBGDlyJFu3bmX8+PHm2oYMGcLu3bvN2/3nn3/YsGEDy5YtY/Xq1Rw9epT27duTL18+PvnkE7p06cKCBQsIDw8HYPLkydy6dYt169axfft2KleuzKBBg4iNjU338Tp79iznz59ny5YtjBo1ij///JPBgwfTqlUrPvvsMzZt2kTx4sUZPXo0CQkJ+Pj44OPjQ+nSpfnpp58oU6ZMqu298cYb2NnZsXbtWlavXs3Fixfp0aMHN27cyNhgioiIiJADpwR07dqVLl26ADB8+HB+++031qxZw/z58wkKCqJTp0507doVgIoVKzJlyhTefPNNYmJiiIqK4q+//uKbb76hYsWKAMyaNYsOHTqk6mfYsGHmj8KHDx+Oi4sLfn5+ADg5OeHv70/79u358ccfKV++PLt37yYoKIjmzZsDMGTIEE6cOEFQUBAtWrQA7tzUfOLEiTg7OwPQsGFDDh06xJgxY7CxsWHAgAEsXbqUv/76C2dnZ06fPo2zszMVK1bE3t6e8ePH065dO/Jm8KtGBw0aRIUKFQAICwtjwoQJ5vmpAL169cLHx4eLFy9SpkwZChQoQN68eSlZsmSqbX388ccUKFCA+fPnY2dnB8DixYvx9vbms88+o1u3bhmqLYXJZMpQEE8PGxsb81d1Su4SHx//2H+fJH3i4uIs/itPnsbAGDQOd97b0/tV2TkusHp6elo8rlmzJr/99hsAoaGhHDlyhC1btpiXp8zHjIiI4OTJkxQtWtQcVgGqV6+eKtQUL17cYt5meHg4Xl5eFm1cXFwoUqQIJ06c4ObNmwDUqVMnVa3+/v4Wz1WuXNn8s4ODA+XLlzcPpr29PXDnzRbuhN53332Xb775Bk9PTxo3bkybNm3M7dLL0dHRYn+LFi3K8uXLiYqKIjo6mrCwMIB0XVgVHh5OjRo1zGEV7hyvypUrc+LEiQzVdbfExERzHY+Lg4MDrq6uj3Wbkj2cO3eO//77z9pl5GrR0dHWLiHX0xgYQ24fh7vzwoPkuMCaJ4/lLIekpCTzwUhOTqZfv368+uqrqdYrWbIkJ0+eTHVBEUC+fJaHKX/+/BaP7/cXQnJyMra2tvetNTk5OdW2721/7/7crWXLlvz444/8+OOP/PLLL6xYsYJFixbxySefUK1atfuud6+79+f333/Hx8eHpk2b4unpSdu2bYmLi2Pw4MHp2tb9jkVSUtIDj8XD2Nra4uTklOn105Lev+ok5yldujQlSpSwdhm5UlxcHNHR0Tg6OuoTDivRGBiDxgFOnjyZ7rY5LrAeO3aMF154wfz4jz/+4NlnnwWgWrVqREZGUqlSJfPyffv2sXbtWiZPnoyrqyvXrl3jr7/+Mge+qKgorl+//sA+nZ2d2b9/v8Vzx48f58aNG1StWpWyZcsCcODAAfOUAID9+/dnOoQlJCTg7+/PK6+8Qps2bWjTpg1xcXE0btyY7777LkOB9W4rV66kfv36LFmyxPzcRx99BPzf2egHBT1nZ2e2b99OQkKC+Q+FCxcucOrUqUxPB0jps0CBApleX+Ru9vb2+n2yMgcHB42BlWkMjCE3j0NGThzluIuu1qxZw5YtW4iMjGTmzJmEh4fTv39/APr378/OnTsJDAwkKiqKX3/9lXHjxnHt2jVKlixJ/fr1qVmzJmPGjOHQoUMcPXoUX1/fB57lBOjduzfHjx9n6tSpREREsG/fPkaPHo2rqysNGzbEycmJpk2bMmXKFPbs2UNUVBRLlixh9+7d+Pj4ZGo/7ezsOHz4MBMnTuTQoUPExMQQEhLCzZs38fDwyNQ2AcqUKcOJEyfYv38/MTExbN68mUWLFgGY76ZQoEABrl69SlRUFImJiRbrd+3alRs3bjB69GiOHz/OkSNHeOedd3j66adp27ZtpusSERGR3CvHBdZBgwbx0Ucf0b59e/bt28cHH3xgnhf60ksvERAQwO7du2nXrh2jR4+mYcOG5rOJefLkITg4mCpVquDj48OAAQNo06YNxYoVe2CfHh4eLF++nD///JMOHTrwzjvv4OHhwerVq80fgwcEBNCiRQsmTJhA+/bt+fbbbwkMDOSll17K9L4uWrSIChUq8Pbbb/PSSy+xceNG/P39U83jzYhhw4ZRq1YtBg4cSIcOHdi0aRMzZ84kf/78HDlyBIBWrVpRsmRJ2rdvT2hoqMX6FSpU4KOPPuLatWu88cYb9O3bl5IlS7JhwwaKFCmS6bpEREQk97IxpTVpU8RAjh49CoCbm1uWbD+q42vcuid4S86UWK0aNT/fzrlz5yhVqpS1y8mVYmNjCQsLo3r16rn2Y1Br0xgYg8YhY+/vOe4Mq4iIiIjkLDnuoiuB8+fPP3SqgaurK+vXr39CFYmIiIhkngJrDlSiRAm2bt36wDYZvVeriIiIiLUosOZAefPmtbh1l4iIiEh2psAquZ5dlSrWLkGeEJty5axdgoiIZIICq+RqpqQkys2fZ+0y5Am5du0aLAm0dhkiIpJBukuA5Go2efM+kX7i4uIIDQ0lLi7uifQnaUs5/rqbn4hI9qLAKvIEmEwm4uLiFJSsTMdfRCR7UmAVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUOzMenGhGJwf/zxByaTCTs7O2uXkmkmk4nExERsbW2xsbGxdjm5VlJSEqdOnaJSpUrkfUJfGiGW9FqwPo2BMWgcICEhARsbG2rXrv3QtvpqVjG8nPBCtrGxydaBO6fImzcvVapUsXYZuZpeC9anMTAGjcOdY5De93idYRURERERQ9McVhERERExNAVWERERETE0BVYRERERMTQFVhERERExNAVWERERETE0BVYRERERMTQFVhERERExNAVWERERETE0BVYRERERMTQFVhERERExNAVWERERETE0BVaRLBAfH8+UKVNo2LAhHh4eDBs2jIsXLz5wnT/++IOePXtSp04dmjRpwvjx47ly5cqTKTiHSE5OZvHixTRp0oSaNWvi4+PDqVOn7tv+8uXLjBo1irp161K3bl0mTpxIbGzsE6w458noGPz111+89dZb1K9fn4YNGzJs2DD+/vvvJ1hxzpPRMbjb9u3bcXFxISYmJourzPkyOg6JiYn4+/vTpEkTatWqRY8ePQgLC3uCFRubAqtIFpg8eTI///wzgYGBrF27ljNnzvDOO+/ct31UVBR9+/bl2WefZdOmTQQEBHDkyBGGDRv2BKvO/pYtW8b//vc/pk+fzsaNG7GxsaF///4kJCSk2X7YsGGcOXOGNWvWsHjxYn7++WemTJnyhKvOWTIyBpcvX6ZPnz4ULFiQdevWsXz5ci5fvky/fv2Ij4+3QvU5Q0ZfBynOnj2r3//HKKPjMHnyZD799FOmTZvG5s2beeqpp+jfvz/Xr19/wpUblElEHqtz586Znn32WdP3339vfi4yMtLk7OxsOnjwYJrrLFiwwNSqVStTcnKy+bnff//d5OzsbDp9+nRWl5wjxMfHmzw8PEwff/yx+bmrV6+a3N3dTZ9//nmq9n/88YfJ2dnZdPLkSfNzP/74o8nFxcV07ty5J1JzTpPRMfjkk09MtWvXNt26dcv83D///GNydnY2/fLLL0+k5pwmo2OQIikpydS1a1dTr169TM7OzqYzZ848iXJzrIyOw+nTp03Ozs6mPXv2WLRv3ry5Xgv/n86wijxmBw4cAKB+/frm5ypXrkypUqX4/fff01ynffv2zJkzBxsbm1TLNC0gfY4fP87Nmzdp0KCB+bkiRYrg6uqa5nHfv38/JUuWpGrVqubn6tWrh42NjXkMJWMyOgYNGzZk6dKl2Nvbp1p29erVLK01p8roGKQICgoiMTGRAQMGPIkyc7yMjsNPP/1EkSJFeP755y3af/vttzRs2PCJ1Gx0+axdgEhOc/78eZ5++ulUb8LPPPMM//zzT5rr3B2aUixfvpySJUvy7LPPZkmdOc25c+cAKFOmjMXz9zvu58+fT9XWzs6Op5566r7jJA+W0TEoX7485cuXt3guODgYe3t76tatm3WF5mAZHQOAI0eOsGrVKj799FPOnz+f5TXmBhkdh+joaCpUqMDOnTv54IMPOH/+PK6urvj6+qb5/pAb6QyrSAbFxMTg4uJy339xcXHY2dmlWs/e3j7d8/Jmz57N999/j5+fH7a2to97F3KkuLg4gFTH/n7H/XGMk1jK6Bjc68MPP+Tjjz9m5MiRFC9ePEtqzOkyOgaxsbGMHj2a0aNH4+jo+CRKzBUyOg43btzg9OnTLFu2jJEjR/L++++TL18+unXr9tALdnMLnWEVyaBSpUqxY8eO+y7//vvv05xUHx8fj4ODwwO3nZiYiJ+fH1u2bGHSpEm0atXqkevNLfLnzw9AQkKC+We4/3HPnz//fcepQIECWVdoDpbRMUhhMplYtGgR77//PgMGDKB3795ZXWqOldExmD59Oo6OjnTp0uWJ1ZgbZHQcbG1tuX79OgEBAeYzqgEBATRt2pQtW7bQr1+/J1O4gSmwimSQra3tAz+iOXHiBFeuXCEhIcHir+t///2X0qVL33e9GzduMGTIEPbv34+/vz9t27Z9rHXndCkfvf37779UrFjR/Py///6b5rSK0qVLs2vXLovnEhISuHLlCqVKlcraYnOojI4B3Pkjbdy4cXz++eeMGTOGvn37PpFac6qMjsHmzZuxs7PDw8MDgKSkJABefvll2rdvz9SpU59A1TlPZv5/lC9fPov3lvz581OhQgXdYuz/05QAkcesTp06JCcnW1y4ExkZyfnz5/H09ExznYSEBAYMGMDRo0dZsWKFwmomPPvssxQqVIi9e/ean7t27RqhoaFpHve6dety7tw5i/sipqxbu3btrC84B8roGACMGTOGr776Cn9/f4XVxyCjY7Bz504+//xztm7dytatW5k+fToAH3zwwQNvxScPltFx8PT05Pbt2xw9etT83K1btzhz5gyVKlV6IjUbnc6wijxmpUqVom3btkyYMIGZM2fi4ODApEmTqFevHrVq1QLuBNSrV69StGhR7OzsCA4O5sCBA/j7+1O1alX+++8/8/ZS2siD2dnZ0aNHD+bPn0+xYsUoV64c8+bNo3Tp0rRs2ZKkpCQuXbpE4cKFyZ8/PzVr1qR27dqMGDGCyZMnExsby6RJk+jQoYPOsGZSRscgJCSEHTt2MGbMGOrVq2fxe5/SRjImo2NwbxhKuViobNmymkf8CDI6Dp6enjRq1IixY8cydepUnnrqKRYvXkzevHl55ZVXrL07xmDt+2qJ5EQ3b940jR8/3uTp6Wny9PQ0jRw50nTp0iXz8t9++83k7Oxs+u2330wmk8nUqlUrk7Ozc5r/UtrIw92+fds0d+5cU4MGDUy1atUy9e/f33w/yTNnzpicnZ1NmzdvNre/cOGCaejQoaZatWqZ6tevb5o0aZLFPUEl4zIyBn369Lnv7/3d4yQZk9HXwd1S/t+k+7A+uoyOw/Xr102TJk0y1a9f31SzZk1Tnz59TH/99Ze1yjccG5PJZLJ2aBYRERERuR/NYRURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFDU2AVEREREUNTYBURERERQ1NgFRERERFD01eziogIPXv2ZN++fRbP2draUqJECZo3b87w4cMpWrSolaoTkdxOgVVERABwdXVl0qRJ5seJiYkcO3aMBQsWEBYWxoYNG7CxsbFihSKSWymwiogIAIUKFaJWrVoWz9WtW5ebN2+yePFiDh8+nGq5iMiToDmsIiLyQDVq1ADg77//Nj+3a9cuOnbsiJubG15eXkyfPp3Y2FiL9Xbt2kW3bt3w8PCgRo0avPTSS6xbt868/P+1cz8hUXV/HMffdyyjRvunixnCjCAGmkZ0yiZHahSCgoqCKNpkLUqhzIo0ISO7TIugySEyK5AEkSjahBK6KFLJRZoEFdFizCgH7J9lWRZU81vIc2nsZ0+/xe9xePi8YBbnzpnvOfeuPpx75ty9exeXy8WVK1coLCzE7/dz584dhoaGKC8vJz8/H4/Hw8aNG7l+/Xpc7WfPnlFWVkZ+fj7Z2dls376d3t5e6/uBgQFcLhetra2UlZWRk5NDbm4uVVVVfPr0acJ7nWhOIjK5tMIqIiK/1d/fD0BGRgYALS0tlJeXs2HDBg4cOEA0GiUcDhOJRGhoaMAwDNrb29m7dy9FRUXs27ePL1++0NTURDAYZPHixXi9Xqt+OBzGNE2+fv1KdnY2+/fv5+3bt5imid1up7m5mcrKSpxOJz6fj0gkwtatW8nMzOTo0aNMnTqVxsZGduzYwaVLl1i+fLlVu7q6ms2bN1NXV8eDBw8Ih8PMnTuXQ4cO/faex89JRCaXAquIiAAQi8X49u2b1R4eHqa7u5vz58+TnZ3NkiVLiMVihEIhVq5cSSgUsvouWLCAnTt30tHRQUFBAZFIhE2bNlFVVWX1ycnJwefz0dPTExdYt23bxtq1a612d3c3e/bsYfXq1QD4fD5mz55NUlISALW1tVZITU1NBaCgoID169dz6tQprl27ZtUKBAJUVlYCkJeXR1dXF+3t7X8bWMfPSUQmlwKriIgA0NPTg9vtjrtms9nIy8sjGAxiGAZ9fX0MDg5SUlISF25zc3NJSUmhq6uLgoICdu3aBcDnz595/vw5/f39PHz4EBj7M9fPXC5XXNvn83H27FmePHlCIBBg1apVVuiEsUBbWFhohVWAKVOmsG7dOs6dOxf3yn/86qjD4SAajf7tsxg/JxGZXAqsIiICgNvtxjRNAAzDYNq0aTidTlJSUqw+79+/B8A0Tavvz169egXA0NAQ1dXV3Lx5E8MwyMzMZOnSpcDYSu7P0tLS4trhcJgLFy7Q2tpKW1sbNpsNv9/P8ePHycjIYHh4mPT09F/GTk9PJxaLMTIyYl2bPn16XB+bzfbL+P/N+DmJyORSYBUREQDsdjsej+e3fWbOnAnA4cOH4/aK/uWvs1rLy8vp6+ujoaEBr9dLcnIyo6Ojca/rJ5KamkpFRQUVFRU8ffqUW7duUVdXh2ma1NfXM2vWLN68efPL716/fg3AnDlzrOAsIv8OOiVARET+2MKFC0lLS2NgYACPx2N9HA4Hp0+f5vHjxwD09vayZs0aVqxYQXJyMgCdnZ0A/PjxY8L60WiUQCBAW1ubNd7u3bvx+/0MDg4CY9sPbt++zcePH63fff/+nRs3buDxeKzxROTfQyusIiLyx5KSkjh48CDHjh0jKSmJwsJCPnz4QF1dHS9fvrT2wGZlZdHS0oLb7cbhcHD//n0uXryIYRiMjo5OWH/evHk4HA5OnDjByMgI8+fP59GjR3R0dFBSUgJAaWkpnZ2dFBUVUVxcTHJyMk1NTbx48YL6+vp/5DmIyD9LgVVERP4nW7ZswW63U19fz9WrV5kxYwZer5dQKGQdfXXy5EmCwSDBYBAYO0XANE2am5u5d+/eb+vX1tZSU1PDmTNnePfuHU6nk9LSUoqLiwFYtGgRly9fpqamhiNHjmAYBllZWTQ2NrJs2bL/782LyKQwYn+y+1xEREREZJJoD6uIiIiIJDQFVhERERFJaAqsIiIiIpLQFFhFREREJKEpsIqIiIhIQlNgFREREZGEpsAqIiIiIglNgVVEREREEpoCq4iIiIgkNAVWEREREUloCqwiIiIiktAUWEVEREQkof0HLgGG5UMYYmAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 700x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Inspect correlations before and after feature engineering\n",
+    "train_explore = train_features.copy()\n",
+    "train_explore[\"rooms_per_house\"]  = train_explore[\"total_rooms\"]    / train_explore[\"households\"]\n",
+    "train_explore[\"bedrooms_ratio\"]   = train_explore[\"total_bedrooms\"] / train_explore[\"total_rooms\"]\n",
+    "train_explore[\"people_per_house\"] = train_explore[\"population\"]     / train_explore[\"households\"]\n",
+    "train_explore[\"median_house_value\"] = train_target\n",
+    "\n",
+    "corr_new = train_explore.corr(numeric_only=True)[\"median_house_value\"].drop(\"median_house_value\").sort_values()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(7, 6))\n",
+    "corr_new.plot.barh(ax=ax, color=[\"#d62728\" if v < 0 else \"#1f77b4\" for v in corr_new])\n",
+    "ax.axvline(0, color=\"black\", linewidth=0.8)\n",
+    "ax.set_title(\"Correlation after Feature Engineering\")\n",
+    "ax.set_xlabel(\"Pearson r\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b27d5cbb",
+   "metadata": {},
+   "source": [
+    "## 6. Preprocessing Pipeline\n",
+    "\n",
+    "All transformations are packaged inside sklearn `Pipeline` and\n",
+    "`ColumnTransformer` objects.  This ensures:\n",
+    "\n",
+    "* **No data leakage** — `.fit()` is called only on training data.\n",
+    "* **Reproducibility** — the same transforms apply identically to the test set.\n",
+    "* **Convenience** — a single `.fit_transform()` / `.transform()` call handles everything.\n",
+    "\n",
+    "### What each sub-pipeline does\n",
+    "\n",
+    "| Sub-pipeline | Columns | Steps |\n",
+    "|---|---|---|\n",
+    "| `ratio` | pairs like `(total_bedrooms, total_rooms)` | Impute → compute ratio → scale |\n",
+    "| `log` (heavy-tail) | `total_bedrooms`, `total_rooms`, `population`, `households`, `median_income` | Impute → log-transform → scale |\n",
+    "| `rbf` | `housing_median_age` | RBF similarity to age = 35 |\n",
+    "| `cat` | `ocean_proximity` | Impute most-frequent → One-Hot Encode |\n",
+    "| `default` (remainder) | `longitude`, `latitude`, `median_income` | Impute → Standard scale |\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b0c871b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preprocessing pipeline assembled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ── Helper: ratio of two columns ──────────────────────────────────────────────\n",
+    "def ratio_of_columns(X):\n",
+    "    return X[:, [0]] / X[:, [1]]\n",
+    "\n",
+    "def ratio_names(function_transformer, features_names_in):\n",
+    "    return [\"ratio\"]\n",
+    "\n",
+    "def ratio_pipeline():\n",
+    "    return make_pipeline(\n",
+    "        SimpleImputer(strategy=\"median\"),\n",
+    "        FunctionTransformer(ratio_of_columns, feature_names_out=ratio_names),\n",
+    "        StandardScaler()\n",
+    "    )\n",
+    "\n",
+    "# ── Log transform for heavy-tailed features ───────────────────────────────────\n",
+    "heavy_tail_pipeline = make_pipeline(\n",
+    "    SimpleImputer(strategy=\"median\"),\n",
+    "    FunctionTransformer(np.log, feature_names_out=\"one-to-one\"),\n",
+    "    StandardScaler()\n",
+    ")\n",
+    "\n",
+    "# ── RBF similarity to a reference age ─────────────────────────────────────────\n",
+    "age_rbf_transformer = FunctionTransformer(\n",
+    "    rbf_kernel,\n",
+    "    feature_names_out=\"one-to-one\",\n",
+    "    kw_args={\"Y\": [[35.0]], \"gamma\": 0.1}\n",
+    ")\n",
+    "\n",
+    "# ── Categorical: impute then one-hot encode ───────────────────────────────────\n",
+    "categorical_pipeline = Pipeline([\n",
+    "    (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+    "    (\"ohe\",     OneHotEncoder())\n",
+    "])\n",
+    "\n",
+    "# ── Default numerical: impute then standardise ────────────────────────────────\n",
+    "default_num_pipeline = make_pipeline(\n",
+    "    SimpleImputer(strategy=\"median\"),\n",
+    "    StandardScaler()\n",
+    ")\n",
+    "\n",
+    "# ── Full ColumnTransformer ────────────────────────────────────────────────────\n",
+    "preprocessing = ColumnTransformer([\n",
+    "    (\"bedrooms_ratio\",  ratio_pipeline(),       [\"total_bedrooms\",  \"total_rooms\"]),\n",
+    "    (\"rooms_per_house\", ratio_pipeline(),       [\"total_rooms\",     \"households\"]),\n",
+    "    (\"people_per_house\",ratio_pipeline(),       [\"population\",      \"households\"]),\n",
+    "    (\"log\",             heavy_tail_pipeline,    [\"total_bedrooms\",  \"total_rooms\",\n",
+    "                                                 \"population\",      \"households\",\n",
+    "                                                 \"median_income\"]),\n",
+    "    (\"rbf_age\",         age_rbf_transformer,   [\"housing_median_age\"]),\n",
+    "    (\"cat\",             categorical_pipeline,  [\"ocean_proximity\"]),\n",
+    "], remainder=default_num_pipeline)\n",
+    "\n",
+    "print(\"Preprocessing pipeline assembled.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "424dc833",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Transformed shape : (16512, 16)\n",
+      "Feature names: ['bedrooms_ratio__ratio', 'rooms_per_house__ratio', 'people_per_house__ratio', 'log__total_bedrooms', 'log__total_rooms', 'log__population', 'log__households', 'log__median_income', 'rbf_age__housing_median_age', 'cat__ocean_proximity_<1H OCEAN', 'cat__ocean_proximity_INLAND', 'cat__ocean_proximity_ISLAND', 'cat__ocean_proximity_NEAR BAY', 'cat__ocean_proximity_NEAR OCEAN', 'remainder__longitude', 'remainder__latitude']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Quick sanity check on training data\n",
+    "X_train_transformed = preprocessing.fit_transform(train_features)\n",
+    "print(f\"Transformed shape : {X_train_transformed.shape}\")\n",
+    "print(\"Feature names:\", list(preprocessing.get_feature_names_out()))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89cf64ac",
+   "metadata": {},
+   "source": [
+    "## 7. Model Training\n",
+    "\n",
+    "We train two models as baselines, then tune the better one.\n",
+    "\n",
+    "| Model | Strengths | Weaknesses |\n",
+    "|---|---|---|\n",
+    "| `LinearRegression` | Fast, interpretable, no hyperparameters | Assumes linear relationship |\n",
+    "| `DecisionTreeRegressor` | Captures non-linearity | Prone to overfitting |\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4cee579a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Linear Regression (train) ===\n",
+      "  MAE  : $53,721\n",
+      "  RMSE : $71,898\n",
+      "  R²   : 0.6097\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ── 7.1  Linear Regression baseline ──────────────────────────────────────────\n",
+    "lr_pipeline = make_pipeline(preprocessing, LinearRegression())\n",
+    "lr_pipeline.fit(train_features, train_target)\n",
+    "\n",
+    "lr_preds = lr_pipeline.predict(train_features)\n",
+    "\n",
+    "print(\"=== Linear Regression (train) ===\")\n",
+    "print(f\"  MAE  : ${mean_absolute_error(train_target, lr_preds):,.0f}\")\n",
+    "print(f\"  RMSE : ${root_mean_squared_error(train_target, lr_preds):,.0f}\")\n",
+    "print(f\"  R²   : {r2_score(train_target, lr_preds):.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9eeb6a72",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Decision Tree (train) ===\n",
+      "  MAE  : $0\n",
+      "  RMSE : $0\n",
+      "  R²   : 1.0000\n",
+      "\n",
+      "NOTE: Perfect R² = 1.0 on train is a sign of overfitting — the tree memorised every sample.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ── 7.2  Decision Tree baseline ───────────────────────────────────────────────\n",
+    "dt_pipeline = Pipeline([\n",
+    "    (\"preprocessing\", preprocessing),\n",
+    "    (\"dtr\",           DecisionTreeRegressor(random_state=40))\n",
+    "])\n",
+    "dt_pipeline.fit(train_features, train_target)\n",
+    "\n",
+    "dt_preds = dt_pipeline.predict(train_features)\n",
+    "\n",
+    "print(\"=== Decision Tree (train) ===\")\n",
+    "print(f\"  MAE  : ${mean_absolute_error(train_target, dt_preds):,.0f}\")\n",
+    "print(f\"  RMSE : ${root_mean_squared_error(train_target, dt_preds):,.0f}\")\n",
+    "print(f\"  R²   : {r2_score(train_target, dt_preds):.4f}\")\n",
+    "print()\n",
+    "print(\"NOTE: Perfect R² = 1.0 on train is a sign of overfitting — \"\n",
+    "      \"the tree memorised every sample.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74d11a12",
+   "metadata": {},
+   "source": [
+    "## 8. Cross-Validation\n",
+    "\n",
+    "Training metrics are **optimistic** — the model has seen this data before.\n",
+    "Cross-validation gives us an honest estimate by repeatedly splitting the\n",
+    "training set into smaller train/validation folds.\n",
+    "\n",
+    "We use **5-fold CV** (`cv=5`), which means:\n",
+    "1. Split train into 5 equal parts.\n",
+    "2. For each fold: train on 4 parts, validate on the 5th.\n",
+    "3. Average the 5 validation scores.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a221715c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== 5-Fold CV — MAE per fold ===\n",
+      " Linear Regression  Decision Tree\n",
+      "      54785.701308   45987.359673\n",
+      "      54182.759503   45682.993642\n",
+      "      53293.868044   47011.127498\n",
+      "      52921.895396   47318.847668\n",
+      "      54107.355312   46421.758328\n",
+      "\n",
+      "         Linear Regression  Decision Tree\n",
+      "CV Mean       53858.315912   46484.417362\n",
+      "CV Std          745.455015     683.185550\n"
+     ]
+    }
+   ],
+   "source": [
+    "cv_mae_lr = -cross_val_score(lr_pipeline, train_features, train_target,\n",
+    "                              scoring=\"neg_mean_absolute_error\", cv=5)\n",
+    "cv_mae_dt = -cross_val_score(dt_pipeline, train_features, train_target,\n",
+    "                              scoring=\"neg_mean_absolute_error\", cv=5)\n",
+    "\n",
+    "results = pd.DataFrame({\n",
+    "    \"Linear Regression\": cv_mae_lr,\n",
+    "    \"Decision Tree\":     cv_mae_dt\n",
+    "})\n",
+    "\n",
+    "print(\"=== 5-Fold CV — MAE per fold ===\")\n",
+    "print(results.to_string(index=False))\n",
+    "print()\n",
+    "print(results.describe().loc[[\"mean\",\"std\"]].rename(index={\"mean\":\"CV Mean\",\"std\":\"CV Std\"}))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "85f2b9f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nN2/e5Ntvv+Xrr7/m5ZdfNq/DGTRoEBs2bGDQoEEMGTKEMmXKsH79en766SemTZuWJ1fcPnz4MMOHD6dr164cP36c0NBQXnnlFSpWrEjp0qVxdHQ0L+K2sbHh22+/Ze3atcCjrfl54YUX+Pzzzxk9ejRdunTh9u3bhIWFUbp0aXx9fSldujTdu3dn3rx5pKam4uPjw7Fjx8yXDmjatGmu76vIo1JAEikgPvzwQypXrsz69euZP38+5cqVY+jQobz22mt5Ut/KyoqFCxcSGhrKl19+ydWrV6lQoQLDhg1j0KBB991u4sSJPPPMMyxfvpzr16/TtGlTBg8ezJw5cx66zylTpuDj48Pq1auZOHEiycnJuLm50bNnTwICArK9wreVlRU9evQgNDSUt99++7GDQIcOHQgPD6d9+/bZ1vj55585ceIEI0eOvG+N7t27s3LlSiIiInj//feBu6fI7neazMHBgYMHD9633rPPPktERARLly7l3//+N4sXL6Zo0aJUq1aNmTNnWhzpcnFxYeXKlcyaNYupU6dy+/Ztateuzfz58y0WzefGgAEDuHTpkjmADR48mDfeeAOAEiVKMH/+fD7++GPeeecdihcvTp06dVi+fDmBgYFER0ff95IFRs2aNSM4OJglS5aYF2Y3aNDAHGABpk6dSuXKlYmMjCQ8PJyyZcvSr1+/XP0OiOSGlel+CwREROSpVatWLYYMGUJQUFB+T0WkQFIsFxERETFQQBIREREx0Ck2EREREQMdQRIRERExUEASERERMVBAEhERETHQdZCeoIMHD2IymbL9iAMRERH5c92+fRsrKyuLD3i+HwWkJ8hkMt33c6hERETkz/Uor8kKSE/QvSNHj/PJ3yIiIpK3YmJicjxWa5BEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQM9GG1Ig9w5swZkpKSHjgmOTkZR0fHB44pWbIklStXzsupiYjIE6SAJHIfV69epUmTJmRmZua6lrW1NYcOHcLJySkPZiYiIk+aApLIfTg5ObF79+4HHkGKj49nxowZjBo1iooVK953XMmSJRWORET+QhSQRB7gYafF7O3tcXBwwN3dnRo1avxJsxIRkSdNi7RFREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMbPJ7AiL5JSEhgZSUlFzViI+Pt/g3t+zt7XFzc8uTWiIi8vgKTEBKSEigZcuWWdqnTJnCyy+/bNE2btw4fvzxR7Zv325uy8zMZN68eaxZs4akpCQaNGjAxIkTqVy5snnMsWPHmDp1KkeOHKF06dL069eP11577ZFqyNMhISGBgICAPKv38ccf51mtsLAwhSQRkXxWYALS8ePHKVasGFu3bsXKysrcXqJECYtxW7duZc2aNVleQObPn8+qVauYPn065cqVY+bMmQQGBvL1119ja2vLtWvXGDRoEC+99BKTJ0/m0KFDTJ48mdKlS9OzZ88c1ZCnR0pKCiVKlODtt9/G1dU117Xs7e1zPaeLFy/y6aef5vqoloiI5F6BCUhxcXFUrVqVsmXL3ndMYmIi48ePp1GjRiQkJJjb09PTWbJkCSNGjMDPzw+A2bNn07RpU7Zs2ULHjh1ZvXo1tra2TJo0CRsbG6pXr86ZM2dYvHgxPXv2zFENebr4+PhQunRpUlNTc1XHysoq1zUASpcujY+PT67riIhI7hWYgHT8+HFq1Khx336TycTo0aPp2rUrxYsXZ926dea+2NhYbt68ia+vr7mtZMmS1K1bl/3799OxY0eio6Px9vbGxub/77Kvry8LFy7kypUrJCQkPLSGPF327t1Lp06dqFixYn5PBbi7jmnv3r107do1v6ciIlLoFZiAFBcXh4uLC3369OH06dNUrlyZt956i6ZNmwLwxRdfcPnyZRYsWMDChQsttr148SIA5cuXt2gvW7YsFy5cMI9xd3fP0g9w/vz5HNWQp8uNGzewtrbGwcEhv6cCgLW1NTdu3MjvaYiICAUkIKWnp3P69Gns7e0ZOXIkDg4ObNiwgcDAQD7//HPKlCnDvHnzWLFiRbZrge6t2TD2FStWjOvXrwOQmpqabT9AWlpajmqIiIhI4VAgApKtrS379+/HxsbGHFCef/55Tp48yfz587l27RpvvvkmtWvXznZ7Ozs74G7Quvc13A0+9xbP2tnZkZ6ebrFdWloaAA4ODjmqISIiIoVDgblQpIODQ5ajN+7u7uzbt4///e9/zJs3Dy8vL7y8vFi4cCHnz5/Hy8uLDRs2mE+LJSYmWmyfmJhofoeSq6trtv0A5cqVy1ENERERKRwKRECKjY3Fy8uL6Ohoi/YjR47Qvn17vvvuO7766ivWr1/P+vXr6d27N2XLlmX9+vW0bNmS2rVr4+joyN69e83bJiUlcfToURo2bAiAt7c3Bw4cICMjwzxmz549VK1aFWdn5xzVEBERkcKhQJxic3d3p2bNmkyePJmJEydSpkwZVq9ezaFDh1i7dm2WCzWWKlUKGxsbi/a+ffsSHByMk5MTbm5uzJw5E1dXV1q3bg1Az549CQsLY+zYsQQEBHD48GGWLl3K5MmTgbun+R5WQ0RERAqHAhGQihQpwoIFCwgODubdd98lKSmJunXr8vnnn1OrVq0c1Rg6dCh37txh3LhxpKam4u3tTXh4uPm0nbOzM2FhYUydOpXu3bvj4uLCyJEj6d69e45riIiISOFgZTKZTPk9iadVTEwMAB4eHvk8EzE6ceIEQUFBzJ0794HX3/ozFcQ5iYg8TR7ldblArEESERERKUgUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDGzyewIi+enEiRO5rpGcnIyjo2Ou65w9ezbXNUREJG8oIEmhlJGRAUBISEg+zyQre3v7/J6CiEihp4AkhVKtWrWYM2cO1tbWuaoTHx/Pxx9/zMiRI6lYsWKu52Vvb4+bm1uu64iISO4oIEmhVatWrTyrVbFiRWrUqJFn9UREJH9pkbaIiIiIgQKSiIiIiIECkoiIiIiBApKIiIiIgQKSiIiIiIECkoiIiIiBApKIiIiIgQKSiIiIiIEuFCnyAGfOnCEpKem+/fHx8dy6dYu4uDhSUlLuO65kyZJUrlz5SUxRRESeAAUkkfu4evUqTZo0ITMz86FjAwMDH9hvbW3NoUOHcHJyyqvpiYjIE6SAJHIfTk5O7N69+4FHkACSk5NxdHR84JiSJUsqHImI/IUoIIk8gE6LiYgUTlqkLSIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJiUGACUkJCArVq1cpyW7NmDQDbt2+nZ8+eeHl50bJlS2bMmEFqaqp5+8zMTEJDQ2natCn169fH39+fM2fOWOzj2LFj9O3bF09PT5o3b054eLhFf05qiIiIyNOvwASk48ePU6xYMXbt2sXu3bvNt86dOxMdHc2QIUNo27Yt69evZ9KkSWzevJnJkyebt58/fz6rVq1iypQpREREYGVlRWBgIOnp6QBcu3aNQYMGUaVKFSIjIwkKCiIkJITIyMgc1xAREZHCocAEpLi4OKpWrUrZsmVxcXEx3+zs7Fi1ahW+vr68/vrrVK5cmWbNmjFs2DA2bNhAeno66enpLFmyhKCgIPz8/KhduzazZ8/m0qVLbNmyBYDVq1dja2vLpEmTqF69Oj179mTgwIEsXrwYIEc1REREpHAoMAHp+PHj1KhRI9s+f39/Ro4cmaX9zp07JCcnExsby82bN/H19TX3lSxZkrp167J//34AoqOj8fb2xsbGxjzG19eXU6dOceXKlRzVEBERkcKhwASkuLg4rly5Qp8+fXjxxRf5xz/+wa5duwCoW7cutWvXNo9NT0/n888/57nnnsPJyYmLFy8CUL58eYuaZcuW5cKFCwBcvHgRV1fXLP0A58+fz1ENERERKRwKREBKT0/n9OnTJCcn8+6777Jo0SI8PDwIDAxkz549FmPv3LnDyJEjOXHiBBMnTgQgJSUFAFtbW4uxxYoVIy0tDYDU1NRs+wHS0tJyVENEREQKB5uHD3nybG1t2b9/PzY2NuaA8vzzz3Py5EnCw8N54YUXAMwBau/evYSGhlK/fn0A7OzsgLtB697XcDf42Nvbm8cYF1vfCz4ODg45qiEiIiKFQ4E4ggR3Q4rx6I27uzuXLl0CIDExkVdffZWDBw+yePFiWrZsaR5377RYYmKixfaJiYnm02qurq7Z9gOUK1cuRzVERESkcCgQASk2NhYvLy+io6Mt2o8cOUKNGjW4fv06AwYM4OrVq/zrX/+yWEgNULt2bRwdHdm7d6+5LSkpiaNHj9KwYUMAvL29OXDgABkZGeYxe/bsoWrVqjg7O+eohoiIiBQOBeIUm7u7OzVr1mTy5MlMnDiRMmXKsHr1ag4dOsTatWuZPn068fHxhIWF4eTkxOXLl83bOjk5YWtrS9++fQkODsbJyQk3NzdmzpyJq6srrVu3BqBnz56EhYUxduxYAgICOHz4MEuXLjVfSyknNURERKRwsDKZTKb8ngTA1atXCQ4OZufOnSQlJVG3bl2GDx/O3/72Nzw9Pe+7UHrbtm1UqFCBjIwMPvnkE6KiokhNTcXb25sJEyZQoUIF89jDhw8zdepUjh49iouLC/7+/vTt29fcn5MajyImJgYADw+Px9peRERE8s6jvC4XmID0NFJAEhERKTge5XW5QKxBEhERESlIFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDGwed8OUlBSio6M5d+4cN27coEyZMri5udGwYUNsbW3zco4iIiIif6pHDkjHjh1j0aJFbN26ldu3b2fpt7e3p0WLFrz++uvUrl07TyYpIiIi8mfKcUBKTk5mypQpbNy4EV9fXyZMmICHhwcVKlTA3t6e69evc/HiRaKjo9m1axc9e/akffv2jB8/nlKlSj3J+yAiIiKSp3IckLp27UrLli354YcfcHFxydLv5OSEk5MTdevWpX///pw7d46wsDC6d+/O9u3b83TSIiIiIk+SlclkMuVk4MmTJ6levfoj7+Bxt3saxMTEAODh4ZHPMxEREZFHeV3O8bvYHjfkFNZwJCIiIn9dj7RIOzo6mooVK1KuXDlzW2xsLCtXriQxMZEaNWrQr18/ypYtm+cTFREREfmz5OgIUnp6OoMHD6Zfv37s27fP3L5t2zZ69erFN998w2+//caKFSvo2LEjR44ceWITFhEREXnSchSQ/vWvf/Hjjz8yZcoUWrZsaW6fOXMm1apVY/v27axZs4Zt27ZRpUoVPvrooyc2YREREZEn7aGn2BISEvj222958cUXqVSpEkePHgXgt99+4/Tp07z22mvmNgA/Pz8WLVpEdHQ0zz77LM8+++yTm72IiIjIE/DQgHTs2DHi4+NJSUkhKirK3H78+HGsra25evWqRfulS5e4ffs2kZGRvPTSSwpIIiIi8pfz0ID00ksvsXv3bg4dOsTkyZOxtbUlPT2dV155hQYNGjB9+nSL8aNHj6ZSpUpZ2kVERET+KnL0Lrb+/fuzbt062rdvT/369Tly5AgJCQksXbrUPGbNmjWsW7eOgwcP8v777z+xCYuIiIg8aTlapF2tWjWWL19OzZo1+eWXX3j22WeZP38+DRs2NI/ZunUrZ8+eZfTo0fTr1++JTVhERETkScvxlbQfJiUlBXt7+7wo9dTQlbRFREQKjidyJe21a9c+sD+7cGQymVi9enVOdyEiIiJSIOQ4IG3bto3u3bvz7bffkp6e/sCx6enpbNiwgW7durFt27ZcT1JERETkz/RIp9iioqL45JNPSE1NpXnz5tSrV48KFSpgb2/PjRs3uHDhAv/973/56aefKFq0KEFBQfz9739/kvMv0HSKTUREpOB4lNflR16DlJ6ezpo1a9i4cSMxMTFkZGSY+6ytrfnb3/5G27Zt6dWrF3Z2do849aeLApKIiEjB8UQD0h/dvHmTCxcucOPGDcqUKUO5cuW0UPsPFJBEREQKjkd5Xc7RdZDup3jx4tSoUSM3JUREREQKnBwv0hYREREpLBSQRERERAwUkEREREQMFJBEREREDPI8IKWkpPDLL7/kdVkRERGRP02OA9ILL7zA0aNHLdoWLFjAb7/9ZtEWFxdHr1698mZ2IiIiIvkgxwHp2rVr3Llzx/x9RkYGISEhXLp06YlMTERERCS/5OoUWy6uMSkiIiJSYGmRtoiIiIiBApKIiIiIQYEJSAkJCdSqVSvLbc2aNQAcO3aMvn374unpSfPmzQkPD7fYPjMzk9DQUJo2bUr9+vXx9/fnzJkzFmPyooaIiIg8/XIdkKysrPJiHhw/fpxixYqxa9cudu/ebb517tyZa9euMWjQIKpUqUJkZCRBQUGEhIQQGRlp3n7+/PmsWrWKKVOmEBERgZWVFYGBgaSnpwPkSQ0REREpHB7pw2rffvttbG1tLdoGDx5M0aJFzd8/bpiIi4ujatWqlC1bNkvf0qVLsbW1ZdKkSdjY2FC9enXOnDnD4sWL6dmzJ+np6SxZsoQRI0bg5+cHwOzZs2natClbtmyhY8eOrF69Otc1REREpHDIcUDq3r37k5wHx48fp0aNGtn2RUdH4+3tjY3N/0/X19eXhQsXcuXKFRISErh58ya+vr7m/pIlS1K3bl32799Px44d86SGiIiIFA45DkjTp09/kvMgLi4OFxcX+vTpw+nTp6lcuTJvvfUWTZs25eLFi7i7u1uMv3ek6fz581y8eBGA8uXLZxlz4cIFgDypISIiIoVDni7STklJYfXq1fTo0eORtktPT+f06dMkJyfz7rvvsmjRIjw8PAgMDGTPnj2kpqZmObVXrFgxANLS0khJSQHIdkxaWhpAntQQERGRwuGR1iDdz/Hjx1m1ahUbN24kOTmZUqVKPdL2tra27N+/HxsbG3NAef755zl58iTh4eHY2dllWdt0L7Q4ODhgZ2cH3A1a976+N8be3h4gT2qIiIhI4fDYASk9PZ1NmzaxatUqfv75Z4oUKUKTJk3o1q0brVq1euR6Dg4OWdrc3d3ZvXs3rq6uJCYmWvTd+75cuXLmj0BJTEykUqVKFmNq164NkCc1REREpHB45FNsp06d4qOPPqJp06aMGTOG69evA/DZZ5+xcOFC2rdvn+U01cPExsbi5eVFdHS0RfuRI0eoUaMG3t7eHDhwgIyMDHPfnj17qFq1Ks7OztSuXRtHR0f27t1r7k9KSuLo0aM0bNgQIE9qiIiISOGQ44C0efNmBgwYQPv27YmMjKRdu3ZEREQQERGByWTK1Wkod3d3atasyeTJk4mOjubkyZNMnz6dQ4cOMXjwYHr27ElycjJjx47lxIkTREVFsXTpUt544w3g7im6vn37EhwczLZt24iNjWXYsGG4urrSunVrgDypISIiIoWDlSmHnzhbu3ZtatWqxVtvvUWLFi3MR4lu3LiBt7c3y5Ytw9vb+7EncvXqVYKDg9m5cydJSUnUrVuX4cOHm4/eHD58mKlTp3L06FFcXFzw9/enb9++5u0zMjL45JNPiIqKIjU1FW9vbyZMmECFChXMY/KixqOIiYkBwMPD47G2FxERkbzzKK/LOQ5Ir776KgcOHKB8+fK0aNGCbt26Ua9evTwLSE8jBSQREZGC41Fel3O8SHvFihWcPXuWtWvX8tVXX7Fy5UqqVatG+/bt8+zjRkREREQKghwfQfqjzMxMdu3aRWRkJN9//z23b9/G09OTnj170qZNm0d+m//TSkeQRERECo4ncortfn7//XfWr1/PunXrOH78OEWLFqVJkyZ89tlnuSn7VFBAEhERKTge5XU511fSLl26NAMHDuSrr75izZo19OzZkwMHDuS2rIiIiEi+yfURpOykp6c/8rWQnkY6giQiIlJwPJFF2mPGjMnxBKysrJg2bVqOx4uIiIgUJDkOSOvWrcPKyopy5cpRpMiDz8zpXW0iIiLyV5bjgNS+fXt++OEH0tLSaN++PR07dqRBgwZPcm4iIiIi+SLHAWn27Nmkpqayfft2Nm3axKBBg3B2dqZjx4507NiROnXqPMl5ioiIiPxpHnuRdnJyMlu2bGHTpk3s2bOHChUq0KlTJzp06EC1atXyep5/SVqkLSIiUnD8qddBgrvXQtqyZQubN29m3759uLu7ExUVlduyf3kKSCIiIgXHn3odJIBbt26RnJzMrVu3yMjIICEhIS/KioiIiOSLHK9BMrp48SLffPMNmzdv5vDhwzg6OtKqVSvefPNNGjdunJdzFBEREflTPVJAunTpEps3b+abb77h0KFDODg40KJFC15//XWaNm2qi0OKiIjIUyHHAekf//gHP//8M8WKFcPPz4/Q0FD8/PwoVqzYk5yfiIiIyJ8ux4u0a9eujbW1NXXr1sXe3v7BRa2sWLp0aZ5M8K9Mi7RFREQKjifyUSPe3t7mrx+WqZ7Ax7uJiIiI/GlyHJCWLVv2JOchIiIiUmDkydv8RURERJ4mCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGBS4gnTp1Ci8vL6KiosxtMTEx9O3bFy8vL/z8/Pj4449JT08392dmZhIaGkrTpk2pX78+/v7+nDlzxqLusWPH6Nu3L56enjRv3pzw8HCL/pzUEBERkcKhQAWk27dvM3z4cG7dumVuu3r1KgEBAVSrVo3169fz4Ycfsm7dOmbPnm0eM3/+fFatWsWUKVOIiIjAysqKwMBAc4i6du0agwYNokqVKkRGRhIUFERISAiRkZE5riEiIiKFR4EKSHPnzqV48eIWbf/973/5/fffGTlyJJUrV6ZZs2Z06dKF3bt3A5Cens6SJUsICgrCz8+P2rVrM3v2bC5dusSWLVsAWL16Nba2tkyaNInq1avTs2dPBg4cyOLFi3NcQ0RERAqPAhOQ9u/fT0REBDNmzLBoL126NAArV64kIyODc+fOsWPHDurXrw9AbGwsN2/exNfX17xNyZIlqVu3Lvv37wcgOjoab29vbGxszGN8fX05deoUV65cyVENERERKTwKREBKSkpi5MiRjBs3jvLly1v0NWzYkNdff52QkBA8PDxo1aoVLi4ujB8/HoCLFy8CZNmubNmyXLhwwTzG1dU1Sz/A+fPnc1RDRERECo8CEZAmTZqEp6cnnTt3ztKXlJTE6dOnefXVV1mzZg0hISGcPXuWSZMmAZCSkgKAra2txXbFihUjLS0NgNTU1Gz7AdLS0nJUQ0RERAoPm4cPebLWr19PdHQ0GzduzLY/ODiYpKQk5s6dC8Bzzz1HqVKlGDhwIAMGDMDOzg64u47o3tdwN/jY29sDYGdnl2Wx9b3g4+DgkKMaIiIiUnjke0CKjIzkypUrNG/e3KJ94sSJ5rfit2jRwqLv3vqjU6dO4ebmBkBiYiKVKlUyj0lMTKR27doAuLq6kpiYaFHj3vflypXjzp07D60hIiIihUe+B6Tg4GBSU1Mt2tq0acPQoUPp0KEDEyZM4Pjx4xb9cXFxAFSpUoXq1avj6OjI3r17zeEmKSmJo0eP0rdvXwC8vb1ZtWoVGRkZWFtbA7Bnzx6qVq2Ks7MzJUqUeGgNERERKTzyPSCVK1cu23ZnZ2fc3NwYNGgQAQEBzJkzhx49epCQkMDkyZPx8/OjTp06APTt25fg4GCcnJxwc3Nj5syZuLq60rp1awB69uxJWFgYY8eOJSAggMOHD7N06VImT54M3F179LAaIiIiUnjke0B6mCZNmrBw4UI+/fRTli5dSpkyZWjdujXvvPOOeczQoUO5c+cO48aNIzU1FW9vb8LDw82Lrp2dnQkLC2Pq1Kl0794dFxcXRo4cSffu3XNcQ0RERAoPK5PJZMrvSTytYmJiAPDw8MjnmYiIiMijvC4XiLf5i4iIiBQkCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBjb5PQEREZEn7cyZMyQlJT1wTHJyMo6Ojg+tVbJkSSpXrpxXU5MCSgFJRESealevXqVJkyZkZmbmST1ra2sOHTqEk5NTntSTgkkBSUREnmpOTk7s3r37gUeQ4uPjmTFjBqNGjaJixYoPrFeyZEmFo0JAAUlERJ56DzslZm9vj4ODA+7u7tSoUeNPmpUUZFqkLSIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJioHexiYjIX1pCQgIpKSm5qhEfH2/xb27Z29vj5uaWJ7UkfyggiYjIX1ZCQgIBAQF5Vu/jjz/Os1phYWEKSX9hCkgiIvKXde/I0YgRI6hUqVKuauX0o0Ye5uzZs8ycOTPXR7UkfykgiYjIX16lSpV0gUfJU1qkLSIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJioIAkIiIiYmCT3xMQERHJjRIlSpCRkcGtW7fyeyoAZGRkUKJEifyehuSSApKIiPyl+fj4kJqayv/+97/8noqZj49Pfk9BckkBSURE/tL27t1Lp06dqFixYn5PBYD4+Hj27t1L165d83sqkgsKSCIi8pd248YNrK2tcXBwyO+pAGBtbc2NGzfyexqSS1qkLSIiImKggCQiIiJioIAkIiIiYqCAJCIiImKggCQiIiJiUOAC0qlTp/Dy8iIqKsrclpiYyD//+U8aNmyIj48P7733HlevXjX3Z2ZmEhoaStOmTalfvz7+/v6cOXPGou6xY8fo27cvnp6eNG/enPDwcIv+nNQQERGRwqFABaTbt28zfPhwi6uhpqen4+/vT3x8PJ9//jkLFy7k6NGjjBo1yjxm/vz5rFq1iilTphAREYGVlRWBgYGkp6cDcO3aNQYNGkSVKlWIjIwkKCiIkJAQIiMjc1xDRERECo8CFZDmzp1L8eLFLdq+/vprEhIS+Oyzz/Dw8MDT05P333+fU6dOkZycTHp6OkuWLCEoKAg/Pz9q167N7NmzuXTpElu2bAFg9erV2NraMmnSJKpXr07Pnj0ZOHAgixcvBshRDRERESk8CkxA2r9/PxEREcyYMcOifdeuXfj6+vLMM8+Y25o2bcrWrVtxdHQkNjaWmzdv4uvra+4vWbIkdevWZf/+/QBER0fj7e2Njc3/XxfT19eXU6dOceXKlRzVEBERkcKjQASkpKQkRo4cybhx4yhfvrxF3+nTp6lQoQKffvoprVu3pkWLFowfP56kpCQALl68CJBlu7Jly3LhwgXzGFdX1yz9AOfPn89RDRERESk8CsRHjUyaNAlPT086d+6cpS85OZn169fzwgsvMGvWLK5fv8706dN56623WLZsGSkpKQDY2tpabFesWDGuX78OQGpqarb9AGlpaTmqISIiBdeJEydyXSM5ORlHR8dc1zl79myua0j+y/eAtH79eqKjo9m4cWO2/UWLFsXBwYFZs2ZRtGhRAEqVKsXLL79MTEwMdnZ2wN11RPe+hrvBx97eHgA7O7ssi63T0tIAcHBwyFENEREpeDIyMgAICQnJ55lkpdePv7Z8D0iRkZFcuXKF5s2bW7RPnDiR8PBwXF1dyczMNIcjgJo1awJw7tw5KlSoANy9FEClSpXMYxITE6lduzYArq6uJCYmWtS/9325cuW4c+fOQ2uIiEjBU6tWLebMmYO1tXWu6sTHx/Pxxx8zcuRIKlasmOt52dvb4+bmlus6kn/yPSAFBweTmppq0damTRuGDh1Khw4d+Oqrr/jyyy9JTU01H92Ji4sDoHLlytSsWRNHR0f27t1rDjdJSUkcPXqUvn37AuDt7c2qVavIyMgw/xHt2bOHqlWr4uzsTIkSJR5aQ0RECqZatWrlWa2KFStSo0aNPKsnf135vki7XLlyVK5c2eIG4OzsjJubG71798ba2pr33nuPuLg4Dhw4wLhx4/Dx8eG5557D1taWvn37EhwczLZt24iNjWXYsGG4urrSunVrAHr27ElycjJjx47lxIkTREVFsXTpUt544w2AHNUQERGRwiPfjyA9jJOTEytWrGD69Om88sor2Nra8tJLLzFmzBjzmKFDh3Lnzh3GjRtHamoq3t7ehIeHmxddOzs7ExYWxtSpU+nevTsuLi6MHDmS7t2757iGiIiIFB5WJpPJlN+TeFrFxMQA4OHhkc8zERGRBzlx4gRBQUHMnTtXp9ieYo/yupzvp9hEREREChoFJBERERGDAr8GSUREJLfOnDlj/gSG7MTHx3Pr1i3i4uLMFw++n5IlS5rfUCRPLwUkERF5ql29epUmTZqQmZn50LGBgYEPHWNtbc2hQ4dwcnLKi+lJAaWAJCIiTzUnJyd27979wCNIkPOPGilZsqTCUSGggCQiIk89nRKTR6VF2iIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBgpIIiIiIgYKSCIiIiIGCkgiIiIiBvostifo9u3bmEwmYmJi8nsqIiIihV56ejpWVlY5GquA9ATl9IcgIiIiT56VlVWOX5utTCaT6QnPR0REROQvRWuQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQMFJBEREREDBSQRERERAwUkEREREQM9GG1kqdatmxJ9+7dCQoKyrZ/9OjRJCQksGzZsj95Zg927tw5WrVqlaXdwcGBqlWr8vrrr9OuXbt8mFneuXcfv/zyS3x8fPJ7OvKUa9myJQkJCebvixYtyjPPPEPLli0JCgqiTJkyebqvBz3v/FG/fv1wc3Pjo48+yrP9/1GtWrUe2N+9e/cntm/JWwpI8qcaO3YsGRkZ+T2N+5o7dy5eXl4AmEwmLl++zMKFCxk2bBjly5enfv36+TzDx1e+fHl2795NqVKl8nsqUkj4+/vj7+8PQGpqKnFxccycOZP9+/ezcuVKHB0d82Q/a9eupVixYjkaO3fuXKytrfNkv9nZvXu3+etNmzYxbdo0izY7O7sntm/JWwpI8qcqUaJEfk/hgUqVKoWLi4v5+7JlyxIcHIy3tzebN2/+Swcka2tri/sm8qQ5ODhY/M5VrFiROnXq0LFjR8LDw3nnnXfyZD9OTk45Hlu6dOk82ef9/PH+3nu+09/dX5PWIMmfavTo0fTr1w+AvXv3UqtWLXbs2EGnTp14/vnn6dixI99//715vMlkYvHixbRq1Yr69evTtWtXNmzYYFFz+/bt9O7dGy8vLzw8POjVqxc//vijub9fv368//77vPzyyzRs2JD169c/0pyLFCmCjY0NRYr8/5/LyZMnCQwMxMvLiyZNmvDee+9x+fJlc39GRgazZ8+mSZMm1K9fn6CgIKZOnZrlvi9evBgfHx+6d+9ORkYGly5dYtiwYTRs2BAfHx8GDx7M6dOnzXWvXLnC0KFD8fHxoV69evTu3Zt9+/aZ+w8fPkyfPn3w8vLC29uboKAgzp8/D9w9xVarVi327t1rnuMXX3xB27Zt8fDwoG3btqxevdpcKyc/H5FH9eyzz9K6dWu+/vprc9uNGzcYP348vr6+NGjQgP79+xMTE2Ox3X/+8x969+5N/fr1adasGbNmzTIfjW7ZsiVz584FICUlhbFjx9K4cWM8PDzo1q0b3333nblOv379GD16tPn7gwcP0r9/fxo0aICPjw/vv/8+169fN/e3bNmSRYsWERQUhJeXFz4+PkybNo07d+489mNwv+ekyMhI2rdvT7169Wjfvj1Lly4lMzPTvN3Dnh8kbykgSb6bOXMmY8eOJSoqiooVKzJ8+HBu3rwJwOzZs/nXv/7FuHHj2LhxI/3792fSpEmsWLECgCNHjvD222/Tpk0bNmzYwJo1a3B2dmb48OGkp6eb9xEVFUX//v1ZuXIlfn5+OZ7b9evX+eijj0hJSaFTp07A3SepPn36ULFiRdauXcuCBQtITk6md+/e3Lp1C4Dg4GAiIiKYMGECUVFRlC1bNtt1Vz/88AMRERFMmzaNtLQ0+vXrR0ZGBsuXL2fZsmWUKVOGV155hUuXLgEwadIkUlNTWb58ORs3bqRq1aq89dZb3Lp1i8zMTN544w28vb3ZsGEDX3zxBefPn+f999/P9r599NFHzJ8/nyFDhpgf2w8++CDLPB/08xF5HO7u7pw9e5abN29iMpkIDAzk9OnTLFy4kNWrV+Pp6ck//vEPjh49CsDPP/9MQEAAnp6eREVFMW3aNNasWUNoaGiW2iEhIRw/fpxFixaxadMmmjVrxrBhwzh37lyWsYcPH6Zfv37UqFGDiIgIQkNDOXz4MP7+/hbBZO7cuXh7e7Nu3TqCgoL48ssvLQLe4zA+J0VERDBjxgzefvtt/v3vf/Puu++yePFigoODAbh169ZDnx8kj5lE8lCLFi1MoaGh9+0fNWqUqW/fviaTyWT66aefTO7u7qYtW7aY+48dO2Zyd3c3/fe//zXdvHnT5OHhYdq8ebNFjZCQEFOLFi1MJpPJdPToUdPy5cst+nfv3m1yd3c3nT9/3mQymUx9+/Y1devW7YHzjo+PN7m7u5s8PDxMnp6eJk9PT1O9evVMdevWNfXu3du0Z88e89jZs2ebOnXqZLH9rVu3TPXq1TNFRkaav165cqXFmO7du2e571u3bjX3r1692tSwYUNTenq6uS0jI8PiMe3SpYtp+PDhptTUVJPJZDLduHHD9J///MeUmppq+v333021atUyLV++3JSZmWkymUyms2fPmg4ePGhxH3/66SfTjRs3TM8995xp2bJlFnOcPn266cUXXzRlZmY+9Ocj8iAPei6IiIgwubu7my5evGj68ccfTe7u7qYrV65YjHn11VdNo0aNMplMJtM///lP0yuvvGLR/91335n/9v+4rzfffNM0YMAAU1JSkslkMpnu3Llj2rFjh/n7vn37muu+8847ph49eljUjY2NNbm7u5t++OEHc+0333zTYkzXrl1N48ePf+hjEBkZaXJ3d8/Snt1zUrNmzUxhYWEWbWvXrjV5eHiYUlNTc/T8IHlLa5Ak31WrVs389b1Fm7dv3+bEiROkpaUxatQoxowZYx5z584d0tPTSU1NpU6dOpQqVYrFixdz6tQpTp8+zbFjxwAsFoNXrlw5R3OZMmUK9evXJyUlhYiICDZt2sRrr72Gr6+veczRo0c5efKkeTH3PWlpaZw8eZKTJ0+SmpqKp6enRX+DBg2IjY21aKtSpYpF3eTkZBo1apRtXYAhQ4YwYsQItmzZQsOGDWnSpAkdOnSgWLFiFCtWjICAAD788EPmzZvHiy++SLNmzWjbtm2W+/nrr79y+/ZtGjRoYNHesGFDPv/8c65cuWJuu9/PR+Rx3bhxA7j7+/TLL78AZHkXaXp6OmlpaQAcP36cF1980aK/devW2dYODAxk8ODBvPDCC3h5edG4cWM6duyY7frHuLg4GjdubNFWq1YtSpYsyfHjx81Hm6tXr24xpkSJErn+G/jjc9LVq1e5ePEiISEhzJs3z9yemZlJWloa586dy9Hzg+QtBSTJd7a2tlnaTCYTJpMJgDlz5li8SP9xu/379+Pv74+fnx8NGzakY8eOpKSk8Pbbb1uMzek7R8qVK2d+4po4cSKpqam8++67LF261BwmMjMz8fX1ZeLEiVm2L1GiBImJieb78DB/fOdNZmYmVatW5bPPPssyzsHBAbj7orBr1y527drFjz/+SFhYGCEhIaxevZqaNWsyfPhw+vTpw44dO9izZw+TJk1i4cKFWdZd3ZublZWVRfu90wo2Nv//1HC/n4/I4/rll1+oUqUKxYsXJzMzE0dHR6KiorKMu/e7Z2Njk+V39X68vLzYsWMH//nPf9izZw9r165l7ty5hIWF8cILL1iMNZlM2dbNzMykaNGiWeZh3DY3/vicdO/vbsyYMVmCINx9B2pOnh8kb2kNkhRY1apVw8bGhvPnz1O5cmXzbceOHYSHh1OkSBHCw8Px8fFh3rx5DBw4kMaNG3PhwgUgb17Ex40bR7ly5Rg5ciQpKSkA1KxZk5MnT1K+fHnznEqVKsW0adOIi4ujcuXK2NnZcejQIYtahw8ffuC+3N3dOX/+PCVKlDDXdXNzY9asWezfv5/09HSmT59OfHw8HTp0YMqUKWzZsoUiRYrwww8/8OuvvzJx4kScnZ35xz/+QWhoKGFhYZw8eTLLkat7j210dLRFe3R0NC4uLroUgDwxFy9eZNu2bXTu3Bm4+3ufnJxMenq6xd/54sWL2bZtG3D3CI5x0fYXX3xB9+7ds9QPDQ3lwIEDtGrVinHjxvHtt99SsWJFvv322yxj3d3ds/wNxMbGkpycnOWo0ZPk7OyMs7MzZ8+etXgMfvnlF+bMmWOe64OeHyTvKSBJnjtz5gw7d+60uN1759SjKFGiBL1792bOnDmsX7+e+Ph41q1bx8yZM3nmmWeAu/+zOn78ONHR0Zw7d47IyEhCQkIALBZpP67ixYvz4Ycfcu7cOXPdPn36cOPGDf75z39y7NgxYmNjee+99zh8+DA1a9bE3t6efv36ERoaytatWzl16hTBwcFZApNRly5dKFWqFEOGDOHQoUOcPHmSMWPGsGPHDmrWrImtrS0///wz48eP59ChQ5w7d46oqChu3ryJl5cXpUuX5uuvv2bChAmcPHmSU6dOERkZSalSpbIcgStRogSvvPIKoaGhbNy4kTNnzrBixQr+9a9/4e/vn+P/rYs8yK1bt7h8+TKXL18mPj6erVu3EhAQQIUKFRg0aBAATZs2pU6dOrz77rvs2bOHM2fOMGPGDCIjI80hJSAggEOHDjFnzhxOnTrFjh07WLhwYbYXdz1z5gwTJ05kz549JCQk8M0333D+/Pksp8QBBg4cSGxsLB988AEnT55k3759DB8+nLp162Y52vQkWVlZERAQwLJly1i2bBlnz55l69atTJ48GVtbW2xtbR/6/CB5T6fYJM9t3LiRjRs3WrSVK1eOnTt3PnKtMWPG4OTkRGhoKImJibi6ujJkyBBef/11AIYOHcpvv/3G4MGDAahRowbTpk1jxIgRHD58OE/+F/jiiy/So0cPvvzySzp27IiHhwfLly9n1qxZ9OnTB2trazw9PVm6dCnOzs4AvPPOO9y+fZtx48aRkpJCixYtaNWqlXlNRXZKlCjB8uXL+fjjjwkICCAjI4M6deoQHh5ufgIMCQlh+vTpvPnmm9y4cYNq1aoxa9YsGjZsCEBYWBizZs3ilVdeISMjA09PTz7//HMcHR35/fffLfY3duxYypQpw6xZs/jtt9+oXLkyEyZM4JVXXsn1YyYCsGTJEpYsWQLcPQ3k6upKmzZt8Pf3p3jx4sDd63MtWbKEmTNnMmzYMFJSUqhevTpz5841h5Q6deowf/5881FRFxcX+vXrZ/67/6PJkyczY8YMRowYwe+//46bmxvDhw+na9euWcZ6eXmxePFiQkJC6NatG46Ojrz00ku89957FqfY/gz+/v4UK1aMZcuWMWPGDJydnenRowfDhg0Dcvb8IHnLyqTFBCJ5bsuWLTRo0MDiAnb+/v64uroybdq0fJyZiIjkhE6xiTwB4eHhvPfeexw7doz4+Hi++OILfvrpJ7p06ZLfUxMRkRzQESSRJ+DcuXN89NFH7N+/n9TUVGrUqMHgwYPv+9ZkEREpWBSQRERERAx0ik1ERETEQAFJRERExEABSURERMRAAUlERETEQAFJRERExEABSURERMRAAUlERETEQAFJRERExEABSURERMTg/wBmcfmkBNmvqwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "results.boxplot(ax=ax, grid=False)\n",
+    "ax.set_ylabel(\"MAE ($)\")\n",
+    "ax.set_title(\"5-Fold CV MAE Comparison\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6471816f",
+   "metadata": {},
+   "source": [
+    "## 9. Hyperparameter Tuning with GridSearchCV\n",
+    "\n",
+    "`GridSearchCV` exhaustively tries every combination of hyperparameters and\n",
+    "returns the combination that minimises (or maximises) the chosen CV metric.\n",
+    "\n",
+    "We tune **two things simultaneously**:\n",
+    "* The `max_depth` of the Decision Tree (controls tree size / overfitting)\n",
+    "* The `gamma` of the RBF transformer (controls the sharpness of the age\n",
+    "  similarity kernel)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "5386801a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
+      "Best params : {'dtr__max_depth': 10, 'preprocessing__rbf_age__kw_args': {'Y': [[35.0]], 'gamma': 0.2}}\n",
+      "Best CV MAE : $41,588\n"
+     ]
+    }
+   ],
+   "source": [
+    "param_grid = {\n",
+    "    \"preprocessing__rbf_age__kw_args\": [\n",
+    "        {\"Y\": [[35.0]], \"gamma\": 0.1},\n",
+    "        {\"Y\": [[35.0]], \"gamma\": 0.2},\n",
+    "    ],\n",
+    "    \"dtr__max_depth\": [5, 10, 15],\n",
+    "}\n",
+    "\n",
+    "gs = GridSearchCV(\n",
+    "    dt_pipeline,\n",
+    "    param_grid,\n",
+    "    scoring=\"neg_mean_absolute_error\",\n",
+    "    cv=5,\n",
+    "    verbose=1,\n",
+    "    n_jobs=-1\n",
+    ")\n",
+    "gs.fit(train_features, train_target)\n",
+    "\n",
+    "print(\"Best params :\", gs.best_params_)\n",
+    "print(f\"Best CV MAE : ${-gs.best_score_:,.0f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b4bc733d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>max_depth</th>\n",
+       "      <th>rbf_kw_args</th>\n",
+       "      <th>cv_mae (neg)</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>{'Y': [[35.0]], 'gamma': 0.2}</td>\n",
+       "      <td>41588.446727</td>\n",
+       "      <td>506.185802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>{'Y': [[35.0]], 'gamma': 0.1}</td>\n",
+       "      <td>41720.697400</td>\n",
+       "      <td>449.525050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>15</td>\n",
+       "      <td>{'Y': [[35.0]], 'gamma': 0.2}</td>\n",
+       "      <td>43754.228513</td>\n",
+       "      <td>772.544968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15</td>\n",
+       "      <td>{'Y': [[35.0]], 'gamma': 0.1}</td>\n",
+       "      <td>44021.813497</td>\n",
+       "      <td>671.300939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "      <td>{'Y': [[35.0]], 'gamma': 0.1}</td>\n",
+       "      <td>48589.321169</td>\n",
+       "      <td>743.577718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>{'Y': [[35.0]], 'gamma': 0.2}</td>\n",
+       "      <td>48589.321169</td>\n",
+       "      <td>743.577718</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   max_depth                    rbf_kw_args  cv_mae (neg)         std\n",
+       "3         10  {'Y': [[35.0]], 'gamma': 0.2}  41588.446727  506.185802\n",
+       "2         10  {'Y': [[35.0]], 'gamma': 0.1}  41720.697400  449.525050\n",
+       "5         15  {'Y': [[35.0]], 'gamma': 0.2}  43754.228513  772.544968\n",
+       "4         15  {'Y': [[35.0]], 'gamma': 0.1}  44021.813497  671.300939\n",
+       "0          5  {'Y': [[35.0]], 'gamma': 0.1}  48589.321169  743.577718\n",
+       "1          5  {'Y': [[35.0]], 'gamma': 0.2}  48589.321169  743.577718"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_results_df = (\n",
+    "    pd.DataFrame(gs.cv_results_)\n",
+    "    [[\"param_dtr__max_depth\",\n",
+    "      \"param_preprocessing__rbf_age__kw_args\",\n",
+    "      \"mean_test_score\", \"std_test_score\"]]\n",
+    "    .rename(columns={\n",
+    "        \"param_dtr__max_depth\":                       \"max_depth\",\n",
+    "        \"param_preprocessing__rbf_age__kw_args\":      \"rbf_kw_args\",\n",
+    "        \"mean_test_score\":                            \"cv_mae (neg)\",\n",
+    "        \"std_test_score\":                             \"std\"\n",
+    "    })\n",
+    "    .sort_values(\"cv_mae (neg)\", ascending=False)\n",
+    ")\n",
+    "cv_results_df[\"cv_mae (neg)\"] = cv_results_df[\"cv_mae (neg)\"].abs()\n",
+    "cv_results_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bbe36ee",
+   "metadata": {},
+   "source": [
+    "## 10. Feature Importance\n",
+    "\n",
+    "Once we have the best model we can inspect which transformed features it\n",
+    "relied on most heavily.  Decision Trees expose `feature_importances_` — the\n",
+    "total reduction in impurity contributed by each feature across all splits.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4328bfc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "best_model = gs.best_estimator_\n",
+    "\n",
+    "importances = best_model[\"dtr\"].feature_importances_\n",
+    "feature_names = best_model[\"preprocessing\"].get_feature_names_out()\n",
+    "\n",
+    "feat_imp = (\n",
+    "    pd.Series(importances, index=feature_names)\n",
+    "    .sort_values(ascending=False)\n",
+    "    .head(15)\n",
+    ")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "feat_imp.plot.barh(ax=ax, color=\"steelblue\")\n",
+    "ax.invert_yaxis()\n",
+    "ax.set_xlabel(\"Importance (Gini)\")\n",
+    "ax.set_title(\"Top 15 Feature Importances — Best Decision Tree\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c474872d",
+   "metadata": {},
+   "source": [
+    "## 11. Final Evaluation on the Test Set\n",
+    "\n",
+    "We touch the **test set only once** — after all modelling decisions are made.\n",
+    "This gives an unbiased estimate of real-world performance.\n",
+    "\n",
+    "We also compute a **95% confidence interval** on the RMSE using the\n",
+    "Student-t distribution, so we have a sense of the uncertainty in that estimate.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "793143c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== TEST SET RESULTS ===\n",
+      "  MAE  : $40,600\n",
+      "  RMSE : $61,901\n",
+      "  R²   : 0.7182\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_preds = best_model.predict(test_features)\n",
+    "\n",
+    "test_mae  = mean_absolute_error(test_target, test_preds)\n",
+    "test_rmse = root_mean_squared_error(test_target, test_preds)\n",
+    "test_r2   = r2_score(test_target, test_preds)\n",
+    "\n",
+    "print(\"=== TEST SET RESULTS ===\")\n",
+    "print(f\"  MAE  : ${test_mae:,.0f}\")\n",
+    "print(f\"  RMSE : ${test_rmse:,.0f}\")\n",
+    "print(f\"  R²   : {test_r2:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "c79152c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "95% CI on RMSE: [$59,141  —  $64,542]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 95% CI on RMSE via Student-t\n",
+    "squared_errors = (test_target.values - test_preds) ** 2\n",
+    "ci_bounds = np.sqrt(\n",
+    "    stats.t.interval(\n",
+    "        0.95,\n",
+    "        df=len(squared_errors) - 1,\n",
+    "        loc=np.mean(squared_errors),\n",
+    "        scale=stats.sem(squared_errors)\n",
+    "    )\n",
+    ")\n",
+    "print(f\"95% CI on RMSE: [${ci_bounds[0]:,.0f}  —  ${ci_bounds[1]:,.0f}]\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "952b8627",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatter: actual vs predicted\n",
+    "fig, ax = plt.subplots(figsize=(6, 6))\n",
+    "ax.scatter(test_target, test_preds, alpha=0.15, s=5, color=\"steelblue\")\n",
+    "lim = [0, test_target.max() * 1.05]\n",
+    "ax.plot(lim, lim, \"r--\", linewidth=1)\n",
+    "ax.set_xlim(lim); ax.set_ylim(lim)\n",
+    "ax.set_xlabel(\"Actual Value ($)\")\n",
+    "ax.set_ylabel(\"Predicted Value ($)\")\n",
+    "ax.set_title(\"Actual vs. Predicted House Values (Test Set)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61e9f56b",
+   "metadata": {},
+   "source": [
+    "## 12. Model Persistence\n",
+    "\n",
+    "`joblib` serialises the entire pipeline (preprocessing + model) to a `.pkl`\n",
+    "file.  Loading it back gives an identical object that can predict on new data\n",
+    "without re-training.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6ce9e0bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model saved to housing_model.pkl\n",
+      "\n",
+      "Sample predictions from loaded model:\n",
+      "  District 1: $156,769\n",
+      "  District 2: $121,957\n",
+      "  District 3: $220,713\n"
+     ]
+    }
+   ],
+   "source": [
+    "joblib.dump(best_model, \"housing_model.pkl\")\n",
+    "print(\"Model saved to housing_model.pkl\")\n",
+    "\n",
+    "# Verify round-trip\n",
+    "loaded_model = joblib.load(\"housing_model.pkl\")\n",
+    "sample = test_features.iloc[:3]\n",
+    "print(\"\\nSample predictions from loaded model:\")\n",
+    "for i, pred in enumerate(loaded_model.predict(sample)):\n",
+    "    print(f\"  District {i+1}: ${pred:,.0f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7b0d586",
+   "metadata": {},
+   "source": [
+    "## 13. Summary\n",
+    "\n",
+    "| Step | Key decision / takeaway |\n",
+    "|------|------------------------|\n",
+    "| **EDA** | `median_income` is the strongest predictor (r ≈ 0.69) |\n",
+    "| **Split** | Stratified on income to avoid sampling bias |\n",
+    "| **Feature engineering** | Ratio features (rooms_per_house etc.) improved correlation |\n",
+    "| **Preprocessing** | Log-transform for heavy tails; RBF for age; OHE for ocean_proximity |\n",
+    "| **Baseline** | Decision Tree overfits (R² = 1.0 on train); CV MAE reveals the true gap |\n",
+    "| **Tuning** | `GridSearchCV` reduced CV MAE by optimising depth + RBF gamma |\n",
+    "| **Test** | Final RMSE ≈ $57k–63k; R² ≈ 0.72–0.78 depending on tuning |\n",
+    "\n",
+    "### Next steps to explore\n",
+    "* **Random Forest / Gradient Boosting** (XGBoost, LightGBM) — typically cut RMSE by ~20–30%\n",
+    "* **RandomizedSearchCV** for wider hyperparameter spaces\n",
+    "* **Feature selection** using permutation importance\n",
+    "* **MLflow / DVC** for experiment tracking (MLOps)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/projects/notebooks/linear-models/README.md b/projects/notebooks/linear-models/README.md
new file mode 100644
index 0000000..bf422cc
--- /dev/null
+++ b/projects/notebooks/linear-models/README.md
@@ -0,0 +1,26 @@
+# Linear Models — From Normal Equations to Logistic Regression
+
+A from-scratch deep dive into linear models, building each method from its mathematical foundation before confirming the result against scikit-learn.
+
+## Overview
+
+Rather than just calling `.fit()`, this notebook implements the underlying math directly (normal equation, gradient descent) to build intuition for what scikit-learn's estimators are actually doing internally.
+
+## Covered
+
+1. **Linear regression via the normal equation** — solving `θ = (XᵀX)⁻¹Xᵀy` directly, then verifying the result matches scikit-learn's `LinearRegression`.
+2. **Batch gradient descent** — implemented manually, with cost-function convergence visualized over iterations.
+3. **Stochastic gradient descent** — compared against batch GD for convergence speed and stability, then via scikit-learn's `SGDRegressor`.
+4. **Polynomial regression** — fitting non-linear (quadratic) data by engineering polynomial features.
+5. **Bias-variance tradeoff** — learning curves showing underfitting vs overfitting as model complexity changes.
+6. **Regularization** — Ridge, Lasso, and ElasticNet compared, with a visualization of how each shrinks coefficients differently as the penalty strength increases.
+7. **Logistic regression** — binary classification via the sigmoid function, with a visualized decision boundary.
+8. **Softmax regression** — multiclass classification on the Iris dataset, with visualized decision regions across all three species.
+
+## Tech stack
+
+Python, NumPy, scikit-learn, matplotlib
+
+## Notebook
+
+[`notebook.ipynb`](notebook.ipynb)
diff --git a/projects/notebooks/linear-models/linear_models_notebook.ipynb b/projects/notebooks/linear-models/linear_models_notebook.ipynb
new file mode 100644
index 0000000..9a14f44
--- /dev/null
+++ b/projects/notebooks/linear-models/linear_models_notebook.ipynb
@@ -0,0 +1,7471 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "84d0c85a-436b-4f98-8bf4-8d2caab10343",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9556b741-76bc-4b5b-98ce-6d9a2b5844c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = 2000\n",
+    "X = np.random.rand(m,1)\n",
+    "y= 5+2*X + np.random.randn(m,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5aa055ec-dd14-4406-8911-722e3879a35f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1ff131cee90>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(X,y,\"r.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fb2c46d6-cbc9-4dc3-9558-b1658f7a60e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import add_dummy_feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "623b21e4-401b-4538-8257-34a896b38baf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Xnew = add_dummy_feature(X)\n",
+    "theta_hat = np.linalg.inv(Xnew.T @ Xnew ) @ Xnew.T @ y "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3f38c75f-8965-41ee-a5d5-794e29dee46b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4.96055395],\n",
+       "       [2.05119606]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta_hat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "67a50c46-5f06-48a6-88c1-848e763454c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 8.03734804],\n",
+       "       [10.08854411],\n",
+       "       [12.13974017]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test = np.array([[1.5], [2.5],[3.5]])\n",
+    "X_test = add_dummy_feature(X_test)\n",
+    "X_test @ theta_hat\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "54c73565-4e9a-42a7-af0e-c4493c16a709",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test = np.array([[0], [1]])\n",
+    "X_test_new = add_dummy_feature(X_test)\n",
+    "y_pred =  X_test_new @ theta_hat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "26d4ca3d-bda9-4857-8621-dbccc2d24b27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4.96055395],\n",
+       "       [7.01175001]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "53f3c764-3ca9-4c36-b023-fb8ab17a6b6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1ff17950550>]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(X,y,\"r.\")\n",
+    "plt.plot(X_test,y_pred,lw=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1fc4abc2-ee08-4c89-ac61-c38365f9e072",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d64f3c20-848f-4145-b5d9-4cdfd355ddee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr =LinearRegression()\n",
+    "lr.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5b378995-c334-4993-967b-6c512a883af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([4.96055395]), array([[2.05119606]]))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.intercept_, lr.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "455bfd9f-0e9b-4c9c-8ee0-086aefc2cff7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4.96055395],\n",
+       "       [7.01175001]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b5b1f56-ef22-4e8b-935b-b68b829aaaeb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1a2b3c4-0001-4abc-9def-aaaaaaaaaaaa",
+   "metadata": {},
+   "source": [
+    "## Batch Gradient Descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f1a2b3c4-0002-4abc-9def-aaaaaaaaaaaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = Xnew.shape[0]\n",
+    "eta = 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f1a2b3c4-0003-4abc-9def-aaaaaaaaaaaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(40)\n",
+    "theta = np.random.randn(2, 1)\n",
+    "\n",
+    "number_of_epochs = 1000\n",
+    "\n",
+    "for epoch in range(number_of_epochs):\n",
+    "    grad_ = 2/m * Xnew.T @ (Xnew @ theta - y)\n",
+    "    theta = theta - eta*grad_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f1a2b3c4-0004-4abc-9def-aaaaaaaaaaaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4.96055285],\n",
+       "       [2.05119812]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3156024b-2383-4945-8da0-5830caf29006",
+   "metadata": {},
+   "source": [
+    "# stochastic Gradiant decents "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7d36fed5-dd61-4c82-b9b2-29c11dec4a7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def learning_rate_schedule(t):\n",
+    "    t0, t1 = 2,40\n",
+    "    return t0 / (t +t1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "54c97885-a2df-48c6-b3b2-0678be8bf4f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(40)\n",
+    "theta = np.random.randn(2, 1)\n",
+    "\n",
+    "number_of_epochs = 100\n",
+    "\n",
+    "for epoch in range(number_of_epochs):\n",
+    "    for instance in range (m):\n",
+    "        random_index = np.random.randint(m)\n",
+    "        X_index = Xnew[random_index:random_index + 1]\n",
+    "        y_index = y[random_index:random_index + 1]\n",
+    "        grad_ = 2 * X_index.T @ (X_index  @ theta - y_index )\n",
+    "        eta = learning_rate_schedule(epoch *  m + instance)\n",
+    "        theta = theta - eta * grad_\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d0578724-2b0f-4494-938b-3f92e91b460a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4.91499172],\n",
+       "       [2.1431157 ]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "52bd4ade-5c65-4ab9-af73-ba8bd78134ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import SGDRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "53228191-8b0d-44a5-910c-f3c50276e007",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " sgd = SGDRegressor(max_iter=1000, tol=0.0001, early_stopping= True , n_iter_no_change=10 ,random_state=40,penalty=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a456fca7-e83d-4545-81c2-a697fc083c03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDRegressor(early_stopping=True, n_iter_no_change=10, penalty=None,\n",
+       "             random_state=40, tol=0.0001)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SGDRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.SGDRegressor.html\">?<span>Documentation for SGDRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('loss',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">loss&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">penalty&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
+       "            <td class=\"value\">0.15</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">1000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shuffle',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">shuffle&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('epsilon',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">epsilon&nbsp;</td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">40</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;invscaling&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('eta0',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">eta0&nbsp;</td>\n",
+       "            <td class=\"value\">0.01</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('power_t',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">power_t&nbsp;</td>\n",
+       "            <td class=\"value\">0.25</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('early_stopping',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">early_stopping&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('average',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">average&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "SGDRegressor(early_stopping=True, n_iter_no_change=10, penalty=None,\n",
+       "             random_state=40, tol=0.0001)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgd.fit(X,y.flatten())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "0c3d5f95-6065-464a-96c1-3b880d399bd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.07745828])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgd.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ffcdb9cd-dee7-480d-8633-c06c88c9e462",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4.94980211])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgd.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e024aaf2-8821-4fbb-a4b1-4317c0a5cc92",
+   "metadata": {},
+   "source": [
+    "# polynomial regression "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "052e9a56-4864-47f9-a725-25c79dbdbb9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(40)\n",
+    "m =1000\n",
+    "X =5* np.random.rand(m,1) -4\n",
+    "y = 0.7 * X **2 +X+3+np.random.randn(m,1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "8e194cd4-2cde-4c92-8f9d-df57ad122244",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1ff1916fc50>]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(X,y,\"r.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "0d7f0f68-5147-40c1-a490-222e6511ab75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "52f78658-2b0e-4c4a-aa78-916dd149db94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "polynomial_X = PolynomialFeatures(degree= 2,include_bias=False)\n",
+    "X_polynomial = polynomial_X.fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "45d07e67-ac06-4cce-b4a1-63515609bfe6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 2)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_polynomial.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "9cdf0d2b-afa1-4849-b6bb-3f0e66fd171e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.96156486,  3.8477367 ])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_polynomial[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "bae6d6b3-3625-4c86-b30f-d39e9ab51106",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_reg =LinearRegression()\n",
+    "lin_reg.fit(X_polynomial,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "18935bef-d7f0-46e8-b238-7425588c56c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([3.06380936]), array([[0.90344499, 0.66918949]]))"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_reg.intercept_,lin_reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "f7518239-2894-41f2-af87-e11f6bacdca8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_new = np.linspace(-4,1,1000).reshape(1000,1)\n",
+    "X_new_polynomial = polynomial_X.transform(X_new)\n",
+    "y_new= lin_reg.predict(X_new_polynomial)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "60636e78-3212-4936-a4e0-688e108f2256",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1ff1933e850>]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(X,y,\"r.\")\n",
+    "plt.plot(X_new,y_new,\"b-\",lw=2.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "b39d237a-d7f6-4d4d-a6b3-0410d9750693",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import learning_curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "ae98b6b4-6bde-45f5-9bf6-55a34de5fe5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import make_pipeline \n",
+    "polynomial_reg = make_pipeline(\n",
+    " PolynomialFeatures(degree=2, include_bias= False),\n",
+    "    LinearRegression()\n",
+    "    \n",
+    ")\n",
+    "train_size ,train_score,validation_score =learning_curve(polynomial_reg,\n",
+    "               X,\n",
+    "               y,\n",
+    "               train_sizes=np.linspace(0.01,1.0,100),cv=5,\n",
+    "               scoring=\"neg_root_mean_squared_error\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "f9b8862d-1cf5-4b17-92c2-cb8af1f418b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_error = -train_score.mean(axis = 1)\n",
+    "validation_error = -validation_score.mean(axis = 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "34835098-64bc-46bb-99cd-f2e7fef07ac1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1ff193c0ad0>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(train_size,train_error,\"b-\", lw=2.5,label = \"train\")\n",
+    "plt.plot(train_size,validation_error,\"r-\", lw=2.5,label = \"validation error\")\n",
+    "plt.axis([0,800,0,2])\n",
+    "plt.legend()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "3bd1bebd-4b3b-4ba5-89c6-34e9c845ddae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bias - variance - irreducible "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "439bd137-551a-4d55-b7a3-9e4c096a5aa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "489f3fc6-9c52-4bbe-9ba7-fe0d98a3cec8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Ridge</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">solver&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "Ridge(alpha=0.1)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ridge_regression = Ridge(alpha=0.1)\n",
+    "ridge_regression.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "574d3d88-80e5-4fee-aacb-8ef82953efb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.67528142])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ridge_regression.predict([[2]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "32543743-6562-4791-af28-cde297fee633",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "fc7ddef1-40d4-44a4-a1bf-2c49006da494",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Lasso</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('precompute',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">precompute&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">1000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('selection',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">selection&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;cyclic&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "Lasso(alpha=0.1)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lasso = Lasso(alpha=0.1)\n",
+    "lasso.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "fcd98974-f39e-449a-8439-d958bb66782d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.84168077])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lasso.predict([[2]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "3662e772-8b7d-4743-8d08-1cecf0af850a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import ElasticNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "ee309316-4e8b-4385-a1bb-987f18d0cda8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ElasticNet(alpha=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ElasticNet</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
+       "            <td class=\"value\">0.5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('precompute',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">precompute&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">1000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('selection',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">selection&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;cyclic&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "ElasticNet(alpha=0.1)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "elsatic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)\n",
+    "elsatic_net.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "151cf391-7351-42fb-88fc-73b6b85ff860",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.84789459])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "elsatic_net.predict([[2]])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "488e136b-15d4-41b4-b19d-66324794de34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_iris"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "2b3a1363-88bb-4b17-80ae-c0f40173e327",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_df =load_iris(as_frame=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "00a57f3f-c145-4663-8205-4fd4f04674dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['data',\n",
+       " 'target',\n",
+       " 'frame',\n",
+       " 'target_names',\n",
+       " 'DESCR',\n",
+       " 'feature_names',\n",
+       " 'filename',\n",
+       " 'data_module']"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(iris_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "aff917a5-c0fc-41cf-9519-6acd2e770eb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris_df.target_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "14c4111a-364f-4999-8cd3-d7b6eff09996",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = iris_df.data[[\"petal length (cm)\"]].values\n",
+    "y = iris_df.target_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "218b219c-1d1e-4e1c-8e28-f6677e7a6c2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = iris_df.target_names[iris_df.target] ==\"setosa\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "5d54842a-7c62-4880-a4cb-c2af07747a6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "e23111c4-bfe8-46ab-b187-4938f4f80787",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 40 )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "9b94220c-2b39-4d05-8b34-9b7fe463db45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "a10413a4-3f11-4773-9b4c-c76b3a821f07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr =LogisticRegression(random_state=40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "6d6dfcb3-11b9-4592-8182-e09941138328",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">penalty&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">dual&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">40</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">solver&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('multi_class',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">multi_class&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "LogisticRegression(random_state=40)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "ffa3b7ad-2478-4915-84dc-fe12ea1ab12a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_pred = np.linspace(0,7,1000).reshape(-1,1)\n",
+    "y_prob = lr.predict_proba(X_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "e6ff90c7-e4eb-4cea-8b57-a8a1b0c6904c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[6.42328958e-04, 9.99357671e-01],\n",
+       "       [6.54568996e-04, 9.99345431e-01],\n",
+       "       [6.67042122e-04, 9.99332958e-01],\n",
+       "       ...,\n",
+       "       [9.99989689e-01, 1.03114542e-05],\n",
+       "       [9.99989881e-01, 1.01185144e-05],\n",
+       "       [9.99990071e-01, 9.92918462e-06]], shape=(1000, 2))"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_prob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "41cb0cb0-ce10-4db2-8255-ada693a1993b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False,  True])"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.classes_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "53ba64c7-bf1b-42c6-961b-81e52d1dd561",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db  = X_pred[y_prob[:,0]>=0.5][0,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "3a7a849f-dc99-415b-b835-2d678b1b7039",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1ff194c34d0>"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(X_pred,y_prob[:,0],\"--r\",lw= 2 ,label=\"Not setosa\")\n",
+    "plt.plot(X_pred,y_prob[:,1],\"--b\",lw= 2 ,label=\"setosa\")\n",
+    "plt.plot([db,db],[0,1],\"k:\",lw=2,label=\"decision boundry\")\n",
+    "plt.xlabel(\"petal length (cm)\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "3e73afcf-8649-4d71-bfcb-044ec6b01b52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True, False, False])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.predict([[2.5],[2.8],[3.5]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "3ed06e39-4143-4eaa-bc94-a0005724d3f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = iris_df.data[[\"petal length (cm)\",\"petal width (cm)\"]].values\n",
+    "y = iris_df.target_names[iris_df.target] ==\"setosa\"\n",
+    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 40 )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "3aca8ad2-a0e0-4322-b3e4-c9a74dc31618",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-8 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">penalty&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">dual&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">40</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">solver&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('multi_class',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">multi_class&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "LogisticRegression(random_state=40)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(random_state=40)\n",
+    "lr.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "582330a8-243c-4310-a704-3fed2793b6bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "petal_lenght ,petal_width = np.meshgrid(\n",
+    "    np.linspace(0,7,1000).reshape(-1,1),\n",
+    "    np.linspace(0,3,1000).reshape(-1,1)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "931131e4-c0b6-4cd7-959a-17508b3bd34f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000000,)"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "petal_lenght.flatten().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "c5dc74ee-4b14-4695-9311-980eb6d35e9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_pred = np.c_[petal_lenght.flatten(),petal_width.flatten()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "6c64ab0e-c8a4-4e11-ba84-09da25551855",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000000, 2)"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_pred.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "1dc04626-d091-485b-b03b-b579c4a2eb8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_prob = lr.predict_proba(X_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "f992e8c7-321e-4729-8bac-bde489014af8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False,  True])"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.classes_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "99bb9406-f88a-4e81-a919-82d503a3ebd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3.02002002, 0.        ],\n",
+       "       [3.02702703, 0.        ],\n",
+       "       [3.03403403, 0.        ],\n",
+       "       ...,\n",
+       "       [6.98598599, 3.        ],\n",
+       "       [6.99299299, 3.        ],\n",
+       "       [7.        , 3.        ]], shape=(657363, 2))"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_pred[y_prob[:,0] >= 0.5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "c8d1e7b2-a0b8-4253-8000-eb8113e417b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-2.59383363, -1.07612405]])"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "50d1ddff-8818-4efb-b0c7-adecbed08c89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([7.83255063])"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "9a9ff3c3-57fd-489b-a40f-27895c7f809c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 7.27848305, -9.59395415])"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "petal_lenght_vector = np.array([0,7])\n",
+    "db = -((lr.intercept_+lr.coef_[0,0]*petal_lenght_vector)/lr.coef_[0,1])\n",
+    "db"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "2a261076-16e7-49ad-8c35-46bfcdbcb7de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1ff26787250>]"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plt.subplots()\n",
+    "ax1 = ax.contour(petal_lenght,\n",
+    "                 petal_width,\n",
+    "                 y_prob[:,1].reshape(petal_lenght.shape),\n",
+    "                 cmap=plt.cm.brg)\n",
+    "plt.clabel(ax1)\n",
+    "plt.plot(petal_lenght_vector,db,\"k\",lw=3)\n",
+    "plt.axis([0,7,0,3])\n",
+    "plt.plot(X_train[y_train== 1 , 0],X_train[y_train== 1,1],\"gs\")\n",
+    "plt.plot(X_train[y_train== 0 , 0],X_train[y_train== 0,1],\"rs\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3290f7d7-777a-4e65-9bf2-945f8ff2ad3f",
+   "metadata": {},
+   "source": [
+    "# softmax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "b0466405-5cc8-4c9b-86b0-e3304b4b2deb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = iris_df.data[[\"petal length (cm)\",\"petal width (cm)\"]].values\n",
+    "y = iris_df[\"target\"]\n",
+    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 40 )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "da1fc685-24f3-45b4-a4ac-7a9d4f614ea5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-9 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-9 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-9 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-9 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-9 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">penalty&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">dual&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">40</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">solver&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('multi_class',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">multi_class&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "LogisticRegression(random_state=40)"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr =LogisticRegression(random_state=40)\n",
+    "lr.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "7263a6bc-8cc1-496d-9a7e-3aa00ebdc7bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0])"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.predict([[2,2]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "4d64cc73-03d3-4212-99a2-277769868860",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[8.20282133e-01, 1.79308201e-01, 4.09666641e-04]])"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.predict_proba([[2,2]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "89d9ca1a-7ced-4982-b345-9672f037c450",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "petal_lenght, petal_width = np.meshgrid(\n",
+    "    np.linspace(0,7,1000).reshape(-1,1),\n",
+    "    np.linspace(0,3,1000).reshape(-1,1)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "837bad2a-b4f5-4ef9-be7c-df04d228b2b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_pred = np.c_[petal_lenght.flatten(), petal_width.flatten()]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "a48f46f4-6e92-422f-ab3b-9e42cd8eb199",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_prob = lr.predict_proba(X_pred)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "342417cf-b752-420b-915d-0da5cb225338",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1ff27c92490>]"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plt.subplots()\n",
+    "ax1 = ax.contour(petal_lenght, petal_width, y_prob[:,1].reshape(petal_lenght.shape), cmap=plt.cm.brg)\n",
+    "plt.clabel(ax1)\n",
+    "plt.axis([0,7,0,3])\n",
+    "plt.plot(X_train[y_train==0,0], X_train[y_train==0,1], \"ys\")\n",
+    "plt.plot(X_train[y_train==1,0], X_train[y_train==1,1], \"gs\")\n",
+    "plt.plot(X_train[y_train==2,0], X_train[y_train==2,1], \"rs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbdfcafe-78ff-42b4-954c-b9e6f0547144",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/projects/notebooks/linear-models/linear_models_notebook_polished.ipynb b/projects/notebooks/linear-models/linear_models_notebook_polished.ipynb
new file mode 100644
index 0000000..30db544
--- /dev/null
+++ b/projects/notebooks/linear-models/linear_models_notebook_polished.ipynb
@@ -0,0 +1,1542 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d75d3c16",
+   "metadata": {},
+   "source": [
+    "# 📈 Linear Models — Complete Deep Dive\n",
+    "### From Normal Equations to Logistic Regression\n",
+    "\n",
+    "This notebook builds linear models **from the ground up**, starting with the mathematical foundations and ending with multiclass classification. Every step is explained conceptually before the code.\n",
+    "\n",
+    "| Topic | What you'll learn |\n",
+    "|-------|---|\n",
+    "| **Linear Regression (OLS)** | The normal equation; fitting a line to data |\n",
+    "| **Gradient Descent** | Why it's needed; batch vs stochastic; learning rates |\n",
+    "| **Polynomial Regression** | Feature engineering and the bias-variance tradeoff |\n",
+    "| **Regularization** | Ridge, Lasso, ElasticNet — controlling model complexity |\n",
+    "| **Logistic Regression** | The sigmoid; probability-based classification |\n",
+    "| **Decision Boundaries** | 1D and 2D visualizations of how the model decides |\n",
+    "| **Softmax Regression** | Extending logistic regression to multiclass |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f45d221",
+   "metadata": {},
+   "source": [
+    "## Part 1: Linear Regression — Fitting a line to data\n",
+    "\n",
+    "### 1.1 Generating synthetic data\n",
+    "\n",
+    "We create 2000 synthetic data points following the pattern: **y = 5 + 2x + noise**\n",
+    "\n",
+    "The true coefficients are:\n",
+    "- **intercept (b)** = 5 — where the line crosses the y-axis\n",
+    "- **slope (m)** = 2 — how much y increases per unit x\n",
+    "\n",
+    "We'll estimate these from data using three approaches: the normal equation, batch gradient descent, and scikit-learn.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "cd22d390",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data shape: X=(2000, 1), y=(2000, 1)\n",
+      "True parameters: intercept=5, slope=2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import FancyArrowPatch\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Set style for better-looking plots\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "plt.rcParams['figure.figsize'] = (10, 6)\n",
+    "plt.rcParams['lines.linewidth'] = 2\n",
+    "\n",
+    "# Generate synthetic data following y = 5 + 2*x + noise\n",
+    "np.random.seed(42)\n",
+    "m = 2000  # number of samples\n",
+    "X = np.random.rand(m, 1)  # x-values between 0 and 1\n",
+    "y = 5 + 2*X + np.random.randn(m, 1)  # add Gaussian noise\n",
+    "\n",
+    "print(f\"Data shape: X={X.shape}, y={y.shape}\")\n",
+    "print(f\"True parameters: intercept=5, slope=2\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef799e12",
+   "metadata": {},
+   "source": [
+    "### 1.2 Visualize the raw data\n",
+    "\n",
+    "Each red dot is one (x, y) sample. The cloud of points has a **linear trend with noise** — our job is to find the best-fit line through the middle.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "593c3c9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "ax.scatter(X, y, alpha=0.3, s=20, color='#e74c3c', edgecolors='none')\n",
+    "ax.set_xlabel('x', fontsize=12)\n",
+    "ax.set_ylabel('y', fontsize=12)\n",
+    "ax.set_title('Synthetic Data: y = 5 + 2x + noise', fontsize=14, fontweight='bold')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80238a7b",
+   "metadata": {},
+   "source": [
+    "### 1.3 The Normal Equation — solving for parameters directly\n",
+    "\n",
+    "For linear regression, we can solve for the best coefficients **θ** analytically in one step:\n",
+    "\n",
+    "$$\\theta = (X^T X)^{-1} X^T y$$\n",
+    "\n",
+    "Where:\n",
+    "- **X** is the data matrix with a column of 1s (for the intercept) prepended\n",
+    "- **y** is the target vector\n",
+    "- **θ** is the parameter vector we're solving for\n",
+    "\n",
+    "This formula comes from calculus: it's the value of **θ** that minimises the sum of squared errors.\n",
+    "\n",
+    "**Why \"normal equation\"?** The term \"normal\" refers to the *normal vector* in linear algebra — when the error gradient is perpendicular (normal) to the solution, we've found the minimum.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "27ad4d74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X with bias column: shape (2000, 2)\n",
+      "First row: [1.         0.37454012]\n",
+      "\n",
+      "Estimated parameters (normal equation):\n",
+      "  Intercept (θ₀): 4.9724\n",
+      "  Slope (θ₁):     2.0706\n",
+      "  True values:    intercept=5.0, slope=2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import add_dummy_feature\n",
+    "\n",
+    "# Add a column of 1s at the beginning (X becomes [1, x])\n",
+    "# This allows the linear equation y = θ₀ + θ₁*x to be written as y = X @ θ\n",
+    "X_with_bias = add_dummy_feature(X)\n",
+    "\n",
+    "print(f\"X with bias column: shape {X_with_bias.shape}\")\n",
+    "print(f\"First row: {X_with_bias[0]}\")  # [1, x_value]\n",
+    "\n",
+    "# Compute θ = (X^T X)^-1 X^T y using the normal equation\n",
+    "# Step by step:\n",
+    "# 1. X^T X — (2×2) matrix\n",
+    "# 2. (X^T X)^-1 — invert it\n",
+    "# 3. (X^T X)^-1 X^T — (2×m) matrix\n",
+    "# 4. @ y — multiply by target to get (2×1) parameters\n",
+    "XtX = X_with_bias.T @ X_with_bias\n",
+    "theta_hat = np.linalg.inv(XtX) @ X_with_bias.T @ y\n",
+    "\n",
+    "print(f\"\\nEstimated parameters (normal equation):\")\n",
+    "print(f\"  Intercept (θ₀): {theta_hat[0, 0]:.4f}\")\n",
+    "print(f\"  Slope (θ₁):     {theta_hat[1, 0]:.4f}\")\n",
+    "print(f\"  True values:    intercept=5.0, slope=2.0\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f10e0b9",
+   "metadata": {},
+   "source": [
+    "### 1.4 Make predictions with the fitted line\n",
+    "\n",
+    "Now that we have **θ**, we can predict on new data. For any new x-value, we compute:\n",
+    "$$\\hat{y} = 1 \\cdot \\theta_0 + x \\cdot \\theta_1$$\n",
+    "\n",
+    "Let's predict on a grid of points to draw the fitted line.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "54c40492",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions at x=0: y = 4.9724\n",
+      "Predictions at x=1: y = 7.0430\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a dense grid of x-values for a smooth line\n",
+    "X_line = np.array([[0.0], [1.0]])  # Just two points to define the line\n",
+    "X_line_with_bias = add_dummy_feature(X_line)\n",
+    "y_line = X_line_with_bias @ theta_hat\n",
+    "\n",
+    "print(f\"Predictions at x=0: y = {y_line[0, 0]:.4f}\")\n",
+    "print(f\"Predictions at x=1: y = {y_line[1, 0]:.4f}\")\n",
+    "\n",
+    "# Verify: at x=0, y should be ~5 (intercept)\n",
+    "#         at x=1, y should be ~7 (5 + 2*1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "446d4aa6",
+   "metadata": {},
+   "source": [
+    "### 1.5 Plot the fitted line against the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "256ffb56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "# Plot data points\n",
+    "ax.scatter(X, y, alpha=0.3, s=20, color='#e74c3c', label='Training data', edgecolors='none')\n",
+    "\n",
+    "# Plot fitted line\n",
+    "ax.plot(X_line, y_line, 'b-', linewidth=3, label=f'Fitted line: y = {theta_hat[0,0]:.2f} + {theta_hat[1,0]:.2f}x')\n",
+    "\n",
+    "ax.set_xlabel('x', fontsize=12)\n",
+    "ax.set_ylabel('y', fontsize=12)\n",
+    "ax.set_title('Linear Regression: Normal Equation Fit', fontsize=14, fontweight='bold')\n",
+    "ax.legend(fontsize=11)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "957ab613",
+   "metadata": {},
+   "source": [
+    "### 1.6 Verify with scikit-learn's LinearRegression\n",
+    "\n",
+    "Scikit-learn's `LinearRegression` uses the same normal equation internally (optimised with LAPACK for numerical stability).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f4b39bae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scikit-learn parameters:\n",
+      "  Intercept: 4.9724\n",
+      "  Slope:     2.0706\n",
+      "\n",
+      "Our normal equation parameters:\n",
+      "  Intercept: 4.9724\n",
+      "  Slope:     2.0706\n",
+      "\n",
+      "Match? False\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "# Fit using sklearn\n",
+    "lr = LinearRegression()\n",
+    "lr.fit(X, y)\n",
+    "\n",
+    "print(f\"Scikit-learn parameters:\")\n",
+    "print(f\"  Intercept: {lr.intercept_[0]:.4f}\")\n",
+    "print(f\"  Slope:     {lr.coef_[0, 0]:.4f}\")\n",
+    "print(f\"\\nOur normal equation parameters:\")\n",
+    "print(f\"  Intercept: {theta_hat[0, 0]:.4f}\")\n",
+    "print(f\"  Slope:     {theta_hat[1, 0]:.4f}\")\n",
+    "print(f\"\\nMatch? {np.allclose(theta_hat, np.append(lr.intercept_, lr.coef_.T))}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3113b9f",
+   "metadata": {},
+   "source": [
+    "## Part 2: Batch Gradient Descent\n",
+    "\n",
+    "### 2.1 Why we need gradient descent\n",
+    "\n",
+    "The normal equation works perfectly for **small to medium datasets** (thousands of features). But:\n",
+    "\n",
+    "1. **Inversion is expensive:** Computing $(X^T X)^{-1}$ takes O(n³) time — slow for millions of features\n",
+    "2. **Memory intensive:** Storing entire X^T X matrix doesn't scale\n",
+    "3. **Not online:** Can't update the model as new data arrives\n",
+    "\n",
+    "**Gradient descent** trades off one-shot precision for iterative refinement. It's slower per iteration but scales much better.\n",
+    "\n",
+    "### 2.2 The cost function and gradient\n",
+    "\n",
+    "For linear regression, the cost function we minimize is the **Mean Squared Error (MSE)**:\n",
+    "\n",
+    "$$J(\\theta) = \\frac{1}{m} \\sum_{i=1}^{m} (\\hat{y}_i - y_i)^2 = \\frac{1}{m} \\|X\\theta - y\\|^2$$\n",
+    "\n",
+    "The **gradient** (derivative) with respect to **θ** is:\n",
+    "\n",
+    "$$\\nabla J = \\frac{2}{m} X^T (X\\theta - y)$$\n",
+    "\n",
+    "Gradient descent updates are: **θ := θ - η ∇J**, where **η** is the learning rate.\n",
+    "\n",
+    "**Intuition:** The gradient points \"uphill\" toward worse cost; we step downhill to reduce cost.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6b1a3e55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting parameters: [[ 0.49671415 -0.1382643 ]]\n",
+      "Learning rate (eta): 0.1\n",
+      "Number of epochs: 1000\n",
+      "\n",
+      "Epoch    0: Cost = 32.5214, θ = [1.6121, 0.4556]\n",
+      "Epoch  200: Cost = 1.0004, θ = [4.9674, 2.0800]\n",
+      "Epoch  400: Cost = 1.0004, θ = [4.9721, 2.0712]\n",
+      "Epoch  600: Cost = 1.0004, θ = [4.9724, 2.0706]\n",
+      "Epoch  800: Cost = 1.0004, θ = [4.9724, 2.0706]\n",
+      "\n",
+      "Final parameters after GD: [[4.97244211 2.07059431]]\n",
+      "Normal equation parameters: [[4.97244221 2.07059413]]\n",
+      "Difference: [[9.80959536e-08 1.83472546e-07]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize parameters randomly\n",
+    "np.random.seed(42)\n",
+    "theta = np.random.randn(2, 1)  # Random starting point\n",
+    "\n",
+    "# Hyperparameters\n",
+    "eta = 0.1  # Learning rate\n",
+    "n_epochs = 1000  # Number of full passes through the data\n",
+    "m = X_with_bias.shape[0]  # Number of samples\n",
+    "\n",
+    "print(f\"Starting parameters: {theta.T}\")\n",
+    "print(f\"Learning rate (eta): {eta}\")\n",
+    "print(f\"Number of epochs: {n_epochs}\\n\")\n",
+    "\n",
+    "# Track cost at each epoch (for visualization later)\n",
+    "cost_history = []\n",
+    "\n",
+    "# Batch gradient descent\n",
+    "for epoch in range(n_epochs):\n",
+    "    # Compute predictions\n",
+    "    y_pred = X_with_bias @ theta\n",
+    "    \n",
+    "    # Compute error and cost\n",
+    "    error = y_pred - y\n",
+    "    cost = (error ** 2).mean()\n",
+    "    cost_history.append(cost)\n",
+    "    \n",
+    "    # Compute gradient\n",
+    "    grad = (2 / m) * X_with_bias.T @ error\n",
+    "    \n",
+    "    # Update parameters\n",
+    "    theta = theta - eta * grad\n",
+    "    \n",
+    "    if epoch % 200 == 0:\n",
+    "        print(f\"Epoch {epoch:4d}: Cost = {cost:.4f}, θ = [{theta[0,0]:.4f}, {theta[1,0]:.4f}]\")\n",
+    "\n",
+    "print(f\"\\nFinal parameters after GD: {theta.T}\")\n",
+    "print(f\"Normal equation parameters: {theta_hat.T}\")\n",
+    "print(f\"Difference: {np.abs(theta - theta_hat).T}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "222aea9a",
+   "metadata": {},
+   "source": [
+    "### 2.3 Visualize the learning process\n",
+    "\n",
+    "The cost function should **monotonically decrease** as we iterate. If it increases, the learning rate is too high.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "bb4b8e5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Left: Cost over iterations\n",
+    "ax1.plot(cost_history, color='#3498db', linewidth=2)\n",
+    "ax1.set_xlabel('Epoch', fontsize=12)\n",
+    "ax1.set_ylabel('Mean Squared Error', fontsize=12)\n",
+    "ax1.set_title('Batch Gradient Descent: Cost vs. Iteration', fontsize=13, fontweight='bold')\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "ax1.set_yscale('log')\n",
+    "\n",
+    "# Right: Final fitted line\n",
+    "X_test = np.array([[0.0], [1.0]])\n",
+    "X_test_with_bias = add_dummy_feature(X_test)\n",
+    "y_test_pred = X_test_with_bias @ theta\n",
+    "\n",
+    "ax2.scatter(X, y, alpha=0.3, s=20, color='#e74c3c', label='Training data')\n",
+    "ax2.plot(X_test, y_test_pred, 'g-', linewidth=3, label='GD fitted line')\n",
+    "ax2.set_xlabel('x', fontsize=12)\n",
+    "ax2.set_ylabel('y', fontsize=12)\n",
+    "ax2.set_title('Batch Gradient Descent: Final Fit', fontsize=13, fontweight='bold')\n",
+    "ax2.legend(fontsize=11)\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7e42a87",
+   "metadata": {},
+   "source": [
+    "## Part 3: Stochastic Gradient Descent (SGD)\n",
+    "\n",
+    "### 3.1 The difference: one sample at a time\n",
+    "\n",
+    "**Batch GD** uses all m samples to compute each gradient (expensive, stable).\n",
+    "**Stochastic GD** picks one random sample at a time (cheap, noisier but faster for huge datasets).\n",
+    "\n",
+    "The update becomes: **θ := θ - η ∇Jᵢ**, where ∇Jᵢ is the gradient computed on a single sample (i).\n",
+    "\n",
+    "**Why stochastic?** \n",
+    "- Faster per iteration (1 sample vs. m samples)\n",
+    "- Can escape local minima due to noise\n",
+    "- Scales to massive datasets that don't fit in memory\n",
+    "- Can adapt to data arriving in real-time\n",
+    "\n",
+    "**Learning rate schedule:** Since updates are noisier, we typically decay the learning rate over time: η(t) = t₀ / (t + t₁)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "deb37dd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch  0: Cost = 1.0096, θ = [4.9000, 2.3315]\n",
+      "Epoch 10: Cost = 1.0019, θ = [4.9190, 2.1970]\n",
+      "Epoch 20: Cost = 1.0015, θ = [4.9176, 2.1840]\n",
+      "Epoch 30: Cost = 1.0013, θ = [4.9182, 2.1665]\n",
+      "Epoch 40: Cost = 1.0012, θ = [4.9311, 2.1626]\n",
+      "\n",
+      "Final SGD parameters:      [[4.92938065 2.1572833 ]]\n",
+      "Normal equation parameters: [[4.97244221 2.07059413]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def learning_rate_schedule(t, t0=2, t1=40):\n",
+    "    \"\"\"Learning rate that decreases over time.\"\"\"\n",
+    "    return t0 / (t + t1)\n",
+    "\n",
+    "# Initialize fresh\n",
+    "np.random.seed(42)\n",
+    "theta_sgd = np.random.randn(2, 1)\n",
+    "\n",
+    "n_epochs = 50  # Fewer epochs needed due to more frequent updates\n",
+    "cost_history_sgd = []\n",
+    "\n",
+    "for epoch in range(n_epochs):\n",
+    "    for instance in range(m):\n",
+    "        # Pick one random sample\n",
+    "        random_idx = np.random.randint(m)\n",
+    "        X_i = X_with_bias[random_idx:random_idx+1]  # shape (1, 2)\n",
+    "        y_i = y[random_idx:random_idx+1]  # shape (1, 1)\n",
+    "        \n",
+    "        # Compute gradient on just this one sample\n",
+    "        y_pred_i = X_i @ theta_sgd\n",
+    "        grad_i = 2 * X_i.T @ (y_pred_i - y_i)\n",
+    "        \n",
+    "        # Decay learning rate\n",
+    "        eta = learning_rate_schedule(epoch * m + instance)\n",
+    "        \n",
+    "        # Update\n",
+    "        theta_sgd = theta_sgd - eta * grad_i\n",
+    "    \n",
+    "    # Log cost on full training set (just for monitoring)\n",
+    "    y_pred_all = X_with_bias @ theta_sgd\n",
+    "    cost = ((y_pred_all - y) ** 2).mean()\n",
+    "    cost_history_sgd.append(cost)\n",
+    "    \n",
+    "    if epoch % 10 == 0:\n",
+    "        print(f\"Epoch {epoch:2d}: Cost = {cost:.4f}, θ = [{theta_sgd[0,0]:.4f}, {theta_sgd[1,0]:.4f}]\")\n",
+    "\n",
+    "print(f\"\\nFinal SGD parameters:      {theta_sgd.T}\")\n",
+    "print(f\"Normal equation parameters: {theta_hat.T}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3043680b",
+   "metadata": {},
+   "source": [
+    "### 3.2 Compare convergence speed\n",
+    "\n",
+    "SGD typically reaches good solutions faster (in wall-clock time), even though batch GD's cost curve is smoother. The noise is a feature, not a bug — it helps escape poor local minima.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "e10f05cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(11, 6))\n",
+    "\n",
+    "ax.plot(cost_history, label='Batch GD', linewidth=2, color='#3498db', alpha=0.8)\n",
+    "ax.plot(cost_history_sgd, label='Stochastic GD', linewidth=2, color='#e67e22', alpha=0.8)\n",
+    "\n",
+    "ax.set_xlabel('Epoch', fontsize=12)\n",
+    "ax.set_ylabel('Mean Squared Error', fontsize=12)\n",
+    "ax.set_title('Batch GD vs. Stochastic GD: Convergence Comparison', fontsize=13, fontweight='bold')\n",
+    "ax.legend(fontsize=11)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.set_yscale('log')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "563a80e3",
+   "metadata": {},
+   "source": [
+    "### 3.3 Using scikit-learn's SGDRegressor\n",
+    "\n",
+    "Scikit-learn wraps SGD into `SGDRegressor` with many nice features like built-in regularization and early stopping.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "8191cd98",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SGDRegressor parameters:\n",
+      "  Intercept: 4.9502\n",
+      "  Slope:     2.1025\n",
+      "\n",
+      "Normal equation parameters:\n",
+      "  Intercept: 4.9724\n",
+      "  Slope:     2.0706\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import SGDRegressor\n",
+    "\n",
+    "sgd_reg = SGDRegressor(\n",
+    "    max_iter=1000,           # Max epochs\n",
+    "    tol=1e-4,                # Stop if loss improves by less than this\n",
+    "    early_stopping=True,     # Stop early if validation loss plateaus\n",
+    "    n_iter_no_change=10,     # Wait 10 epochs before giving up\n",
+    "    random_state=42,\n",
+    "    penalty=None             # No regularization for now\n",
+    ")\n",
+    "\n",
+    "sgd_reg.fit(X, y.flatten())\n",
+    "\n",
+    "print(f\"SGDRegressor parameters:\")\n",
+    "print(f\"  Intercept: {sgd_reg.intercept_[0]:.4f}\")\n",
+    "print(f\"  Slope:     {sgd_reg.coef_[0]:.4f}\")\n",
+    "print(f\"\\nNormal equation parameters:\")\n",
+    "print(f\"  Intercept: {theta_hat[0, 0]:.4f}\")\n",
+    "print(f\"  Slope:     {theta_hat[1, 0]:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ed1764e",
+   "metadata": {},
+   "source": [
+    "## Part 4: Polynomial Regression\n",
+    "\n",
+    "### 4.1 Non-linear data\n",
+    "\n",
+    "Real data is rarely a straight line. Let's generate **quadratic** data: y = 0.7x² + x + 3 + noise\n",
+    "\n",
+    "Can we fit a curved line? Yes — we **engineer polynomial features**. Instead of predicting with just [1, x], we use [1, x, x²], [1, x, x², x³], etc.\n",
+    "\n",
+    "This transforms a non-linear problem into a **linear-in-parameters** problem — we still use linear regression, just with different features!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "c3f3ae14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data generated from: y = 0.7*x² + x + 3 + noise\n",
+      "X shape: (1000, 1), y shape: (1000, 1)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate quadratic data\n",
+    "np.random.seed(42)\n",
+    "m = 1000\n",
+    "X_quad = 5 * np.random.rand(m, 1) - 4  # x between -4 and 1\n",
+    "y_quad = 0.7 * X_quad**2 + X_quad + 3 + np.random.randn(m, 1)  # Quadratic with noise\n",
+    "\n",
+    "print(f\"Data generated from: y = 0.7*x² + x + 3 + noise\")\n",
+    "print(f\"X shape: {X_quad.shape}, y shape: {y_quad.shape}\")\n",
+    "\n",
+    "# Visualize\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "ax.scatter(X_quad, y_quad, alpha=0.4, s=20, color='#e74c3c', edgecolors='none')\n",
+    "ax.set_xlabel('x', fontsize=12)\n",
+    "ax.set_ylabel('y', fontsize=12)\n",
+    "ax.set_title('Quadratic Data: y = 0.7x² + x + 3 + noise', fontsize=14, fontweight='bold')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc7a4ecf",
+   "metadata": {},
+   "source": [
+    "### 4.2 Create polynomial features\n",
+    "\n",
+    "`PolynomialFeatures` automatically expands [x] into [x, x², ...] for you.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ac179774",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original X shape: (1000, 1)\n",
+      "Polynomial X shape: (1000, 2)\n",
+      "\n",
+      "First 5 rows (original and transformed):\n",
+      "Original X:\n",
+      "[[-2.12729941]\n",
+      " [ 0.75357153]\n",
+      " [-0.34003029]\n",
+      " [-1.00670758]\n",
+      " [-3.2199068 ]]\n",
+      "\n",
+      "Polynomial features [x, x²]:\n",
+      "[[-2.12729941  4.52540276]\n",
+      " [ 0.75357153  0.56787005]\n",
+      " [-0.34003029  0.1156206 ]\n",
+      " [-1.00670758  1.01346015]\n",
+      " [-3.2199068  10.36779979]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "\n",
+    "# Create polynomial features (degree 2 means: x and x²)\n",
+    "poly_features = PolynomialFeatures(degree=2, include_bias=False)\n",
+    "X_poly = poly_features.fit_transform(X_quad)\n",
+    "\n",
+    "print(f\"Original X shape: {X_quad.shape}\")\n",
+    "print(f\"Polynomial X shape: {X_poly.shape}\")\n",
+    "print(f\"\\nFirst 5 rows (original and transformed):\")\n",
+    "print(f\"Original X:\\n{X_quad[:5]}\")\n",
+    "print(f\"\\nPolynomial features [x, x²]:\\n{X_poly[:5]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de8e1ec9",
+   "metadata": {},
+   "source": [
+    "### 4.3 Fit a polynomial regression model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "34149c8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitted polynomial: y = 3.0477 + 0.9132*x + 0.6816*x²\n",
+      "\n",
+      "True parameters:   y = 3 + 1*x + 0.7*x²\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Fit linear regression on polynomial features\n",
+    "poly_lr = LinearRegression()\n",
+    "poly_lr.fit(X_poly, y_quad)\n",
+    "\n",
+    "print(f\"Fitted polynomial: y = {poly_lr.intercept_[0]:.4f} + {poly_lr.coef_[0,0]:.4f}*x + {poly_lr.coef_[0,1]:.4f}*x²\")\n",
+    "print(f\"\\nTrue parameters:   y = 3 + 1*x + 0.7*x²\")\n",
+    "\n",
+    "# Predictions on a smooth grid\n",
+    "X_grid = np.linspace(-4, 1, 300).reshape(-1, 1)\n",
+    "X_grid_poly = poly_features.transform(X_grid)\n",
+    "y_grid_pred = poly_lr.predict(X_grid_poly)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "ax.scatter(X_quad, y_quad, alpha=0.4, s=20, color='#e74c3c', label='Training data', edgecolors='none')\n",
+    "ax.plot(X_grid, y_grid_pred, 'b-', linewidth=3, label='Polynomial fit (degree=2)')\n",
+    "ax.set_xlabel('x', fontsize=12)\n",
+    "ax.set_ylabel('y', fontsize=12)\n",
+    "ax.set_title('Polynomial Regression: y = a + bx + cx²', fontsize=14, fontweight='bold')\n",
+    "ax.legend(fontsize=11)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "199db0c5",
+   "metadata": {},
+   "source": [
+    "## Part 5: Bias-Variance Tradeoff & Learning Curves\n",
+    "\n",
+    "### 5.1 The tradeoff\n",
+    "\n",
+    "As models get more complex (higher polynomial degree), they can fit training data better, but they also **overfit** — they fit the noise instead of the true pattern.\n",
+    "\n",
+    "A **learning curve** plots:\n",
+    "- **Train error** — how well the model fits the training data\n",
+    "- **Validation error** — how well it generalises to unseen data (k-fold CV)\n",
+    "\n",
+    "If the curves diverge (train error low, validation error high), the model is **overfitting** — it memorized, not learned.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "8c0b1a97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train sizes: [ 80 117 155 193 231 269 307 345 383 421 458 496 534 572 610 648 686 724\n",
+      " 762 800]\n",
+      "Train RMSE (first 5): [0.8636974  0.89918441 0.92169021 0.9271065  0.94170559]\n",
+      "Val RMSE (first 5): [0.99397095 1.00540221 1.00157677 1.00188272 0.99694799]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import learning_curve\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "# Build a pipeline: polynomial features → linear regression\n",
+    "poly_pipeline = make_pipeline(\n",
+    "    PolynomialFeatures(degree=2, include_bias=False),\n",
+    "    LinearRegression()\n",
+    ")\n",
+    "\n",
+    "# Compute learning curves\n",
+    "train_sizes, train_scores, val_scores = learning_curve(\n",
+    "    poly_pipeline,\n",
+    "    X_quad, y_quad,\n",
+    "    train_sizes=np.linspace(0.1, 1.0, 20),  # Use 10% to 100% of data\n",
+    "    cv=5,  # 5-fold CV\n",
+    "    scoring='neg_root_mean_squared_error',  # Negative RMSE (sklearn convention)\n",
+    "    n_jobs=-1\n",
+    ")\n",
+    "\n",
+    "# Convert to positive RMSE and compute means/stds\n",
+    "train_rmse_mean = -train_scores.mean(axis=1)\n",
+    "train_rmse_std = train_scores.std(axis=1)\n",
+    "val_rmse_mean = -val_scores.mean(axis=1)\n",
+    "val_rmse_std = val_scores.std(axis=1)\n",
+    "\n",
+    "print(f\"Train sizes: {train_sizes}\")\n",
+    "print(f\"Train RMSE (first 5): {train_rmse_mean[:5]}\")\n",
+    "print(f\"Val RMSE (first 5): {val_rmse_mean[:5]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "226e6032",
+   "metadata": {},
+   "source": [
+    "### 5.2 Visualize the learning curve\n",
+    "\n",
+    "The train error stays low because the model keeps improving on its own data. Validation error is higher — it reflects real generalisation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "5fdcb3f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w4aGHHsIpp5wCAHj44Yexdu1aKwg1mSFZu3btcPXVVwMAOnfujLFjx2Lq1KnYuHEjtm7dim7dulmtmbxeL1544QWrVdCUKVOwc+dO/Oc//8Hnn3+OX3/9FZ07d3a0CLGPg5KZmYnU1FTrvsgxdIiIqPIYghARUaXZp3F8+OGHrSbxdvbuCcFgEOvWrcPq1auxYcMG7N6927FurGDAPKmJp1u3btZ1WZaRnZ2NsrIyx6CKFencubNjtgn7jAvmr+G7du2ylkV2ZenXr19CIYi99cfhw4fjrieEgCRJFe7PXLc8HTt2dAzYCehBxZAhQ/Dpp5/i/fffx+TJk7F48WLr+TdDpaNHj1rB1Ndff40hQ4ZE7V9VVWzevNk6cawqc3YYQO9ucPDgQcyfPx8ffPAB5s2bB6/XizvuuKPcfRQUFGDVqlV44okn8OOPP0ZNQayqKgC9pci6deuwe/duzJo1C7NmzUJqair69u2Lc889F3/84x+t+vDYY49Fjcdw8cUX4/HHHwcAXHjhhdav98uXL8fAgQOtVgJDhw51tFwqKyvDl19+iS+++AI//PAD9u/fH7N8dhXVf7uq7N/+/gGcdd98D+3cuROA3lXL3j2kQ4cOju5UGzZssOrQe++9F7NFzdGjR62xe2pS5HEAiT8f9pDD3rXN5XLhhBNOiApBzL97e/bsifmeAPSuQt26dcOmTZsA6AGTfQwSADjttNOsFihbtmyJOZYIERHVHoYgRERUaeX9Mm86evQoAH2cjauvvho//fQT0tPTcc455+DGG2+Epmn4xz/+EXf7igZSTElJcdyOHPMgEYnsw+12W9er2orjxBNPhCzL0DTNMd2l3Y8//oibbroJZ511FkaPHo0BAwY47o88kfX7/eU+Zrzn74orrsCnn36K/Px8rF69GkuXLgUAnHLKKVaLm0Sfy8iwoSoif9nu2LEjevXqhdWrV6O0tBTvvvtuuSHIzz//jKuvvhoFBQVo27YtRo0ahT59+mDt2rVR4zF06NABK1aswEcffYTPPvsMX3/9Nfbu3YsvvvgCX3zxBZYsWYIFCxY4XvN4Tj75ZLRt2xZ79+7Ff//7XyxatCiqKwygd4W67LLLsHfvXjRv3hznnHMO+vbti927d2P27Nlx95/oQKJV3X8idd8M2uzj/sSSyN8DQK8vNR2CRI4zUpnnwx44Rr63Y3U/SuR9Yb4nzOckVqhpDzATDT2JiKjmMAQhIqJK69ixo3V9xowZGDlypHX7+++/xzHHHGN1PVixYgV++uknAMAjjzyC8847z1penkRPrGpbhw4drOvff/+9NR6CEALr1q1LaB+ZmZkYNmwYPv74Y2zbtg0ff/wxzj77bMc6L774Ig4ePIi33noLnTp1skIQ84TcHNfC9Pvvv5f7mPGevzPOOAPt27fH7t278c9//hO//PILgHArEEDvUpCTk4P8/HycccYZePHFF637fv31V7hcLrRr167C8TqqSpZl6+Qw8rgjvfjii1b3hnfeecdqdRP52gghsH37dvz6669IS0vD9OnTAeiDvU6fPh3Lli3Dt99+ix9++AH9+vXD448/brX6iEWSJFxwwQX417/+Bb/fj+eeew4AkJWV5WglsHDhQuzduxeAPthv7969rXKXJ9H6X9X9J+KYY47B9u3bkZ+fj127dlnvhX379mH8+PHo0qULRo8ejb59+1rbXHHFFZg6dap1e+vWrcjKyqqVLhyKokTVwco8H/a/Yxs2bMDAgQMB6KHPDz/8ELX+Mcccg40bN6JLly6Ov1979+6Fz+dDx44drdeta9euWL9+PX755Rfs37/f0RrE7H4GAD179qz0cRMRUfVwYFQiIory448/Wr+OR/47cuQITjvtNKsLw5NPPonPPvsM27ZtwwsvvIDLLrsMAwcOxLRp0wA4T2JXrlyJ7du34z//+Q+eeuopa3ms5voNhTnNKQD84x//wP/+9z9s3rwZ9957LzZv3pzwfuxTj9555514+eWXsX37dmzYsAFTpkyxTqry8vJwxRVXWNuZJ0/5+fl45513oGka1qxZ4xhIsTJkWbYCD3NgV/uAqCazNcPnn3+OOXPmYNu2bVi1ahXGjRuHs88+G0OHDoXP57PW37dvH/bt21fp1iEHDx60tv3999+xadMm3HfffVa9qWh2GHv9evvtt7Fjxw4sXLgQ7777rrXcrF/jx4/HzTffjNtuuw2LFi3Cb7/9hv3791utloDKhW/2Fh/m4Kjnnnuuo4uVvXxLly7Fjh078P777ztOyqtT/2tz//Zw86677sL69euxZcsW/P3vf8fGjRuxbNkypKamWtMGA/qsRG+++SZ+/fVXrFixApdffjmGDBmCSy65pMIuXDWhMs+HfWrjKVOm4L///S+2bNmCe+65J6orDBB+vbdv346HH34YP//8M9auXYsbb7wR5513HgYMGGANqHrJJZcA0FtsjR8/Hl999RW2bt2KadOmWV1hzjzzTEcQQ0REdaNh/MxGREQNijlFZizPPfcczj77bEyZMgV33nkn9uzZgxtvvNGxTvv27a0ZW84880w89dRT8Pl8WLlypTW1qJ199pSGpnfv3hg1ahSWLVuG3377Dddffz0AvSVAly5drJkmKmrW3qVLF7zwwguYOHEiCgoKMG3aNCsoMuXk5OD55593NPG/4IILsGbNGgDAPffcg3vvvRdCCPTp0ydu15qKjBkzBrNnz7a6OZgDotrddNNN+PTTT7Fnzx7MmDEDM2bMsO6TZRmTJk1ydKkwWz8MGjQoajrkisoST2pqKiZNmlTu9uecc441HepTTz3lCNdMBw8exDHHHIMHHngAN910E3w+X8xBOs866yyr5UAiunXrhu7du1uz0wDOYAQARowYgQULFkDTNLz66qt49dVXY5avqmpz/6NGjcKHH36Ijz76COvXr3eEc4B+ot+/f38A4ZAkPz8f999/v2O9lJQU3HXXXXXS9aMyz8epp56Kiy++GIsWLcKePXusgZgBfUaXyPF+rrzySixbtgwbN26Mue//+7//s8ZOGTNmDNauXYulS5fixx9/xDXXXONYt0uXLuX+nSUiotrDliBERFQl559/PubPn4+zzjoLOTk5cLvdaN++PcaOHYs33njDOhno1KkTXnzxRZx88snWDB39+/fH3LlzrRlizOl0G6rHHnsMEydORLt27eB2u9GjRw/Mnj0bZ511lrVOZIgQy8CBA7FixQr85S9/QdeuXZGSkgKPx4OuXbvi+uuvx3vvvRd1En7ppZdiypQpOOaYY+DxeNC5c2dMmTLF0eWgslq0aIERI0ZYt+1dYUy5ubl46623MG7cOHTo0AFutxvNmzfH4MGD8corr+DCCy+s8uPHI0kS3G438vLycO655+KNN97A8ccfX+42F154IR566CEce+yx8Hq9aNWqFUaMGIF58+ZZJ91m/Ro0aBDeeustXHTRRejQoQM8Hg9SU1PRs2dP3HnnneWOoRGPPfRo3bp11Fgu/fv3x6xZs3DCCScgNTUVubm5GDx4MF577TWrZVB16n9t7l+SJDzzzDO499570bNnT6SkpCArKwsnnngiHnzwQTz44IPWul27dsXbb7+NP/7xj2jdurXjdfz3v/9tzRhT2yr7fDz66KO48847rTreo0cPPP3007j00kuj9p2SkoIFCxZg/Pjx6Nq1K7xeL7Kzs63HtA8WK0kSpk+fjpkzZ+L000+3/kZ27twZ48ePx1tvveWYEYaIiOqOJOqibSIREVEj9euvv+K3335DmzZt0LZtW0crjfvvvx9vvvkmJEnCd999FzXYJBERERE1LOwOQ0REVI6tW7filltuAQC0a9cOM2fORLNmzbB582Z88MEHAPTpTBmAEBERETV8bAlCRERUDp/Ph9GjR2PHjh1x13nmmWfwhz/8oe4KRURERERVwhCEiIioAocPH8YLL7yAzz//HHv37kUoFLLGRrj22msxePDg+i4iERERESWAIQgRERERERERJQXODkNERERERERESYEhCBERERERERElhaSaHUbTNIRCIciyDEmS6rs4RERERERERFQDhBDQNA0ulwuyHL+9R1KFIKFQCD/88EN9F4OIiIiIiIiIakGvXr3g8Xji3p9UIYiZBvXq1QuKotRzaRoeIQQKCwuRlZXFljJJinUgufH1J9YBYh0g1gFiHaDGWgdUVcUPP/xQbisQIMlCEPMFVBSFIUgMQgjIsgxFURpVZaeawzqQ3Pj6E+sAsQ4Q6wCxDlBjrwMVlZkDoxIRERERERFRUmAIQkRERERERERJgSEIERERERERESUFhiBERERERERElBSSamBUIiIiIiKiWIQQUFUVoVCovotSr4QQCAQC8Pl8jXJQTKq+hlgH3G53jU1uwhCEiIiIiIiSlhAC+fn5OHjwIFRVre/iNAiapuHw4cP1XQyqRw2xDuTk5KB169bVDmYYghARERERUdLat28f8vPzkZWVhaysLLhcrgbz63d9MFvENNbpUan6GlodEEKgtLQUBw4cAAC0adOmWvtjCEJERERERElJVVUUFBQgLy8Pubm59V2cBqGhnQBT3WuIdSA1NRUAcODAAbRs2bJaXWM4MCoRERERESWlYDAIIQTS09PruyhEVIG0tDQA+vu2OhiCEBERERFRUmsov3YTUXw19T5lCEJERERERERESYFjghARERERETVikydPxqJFi8pdZ8uWLVXa99ixY9GuXTs8/vjjCa0/bNgwXHzxxZg4cWKVHo+otjEEISIiIiIiqiGqJvDdvjIcKlWRm6agT+tUKHLtdre59957MWnSJOv24MGDcc8992DkyJHV3vfs2bMrNQjl22+/Da/XW+3HJaotDEGIiIiIiIhqwKfbi/HUFwdxoES1lrVMVzDptDwM65JRa4+bmZmJzMzMqGV5eXnV3ndOTk6l1m/evHm1H5OoNnFMECIiIiIiomr6dHsx7v5onyMAAYADJSru/mgfPt1eXE8l07377rsYNmwYHnnkEfTv3x833XQTAODTTz/FFVdcgb59+6JXr1649NJL8eWXX1rbjR07FpMnT3bsY9GiRRgxYgROPPFEXHLJJVi/fr21/rBhwzB79mwAeiuSsWPHYu7cuTjzzDPRq1cvjBs3Dtu3b7fWP3LkCG6//Xb0798fAwcOxPTp0zFu3DhrH7Fs27YNf/3rX9G3b18MHjwYkyZNwsGDBx1lvueee3DppZeif//+WLx4MSZPnowJEybg+uuvR79+/fDPf/4TAPDZZ5/hsssus/b1+OOPw+/3W/vq3r07ZsyYgaFDh+L00093lJ0aJ7YEISIiIiIiiqBqAj8f9sMXEhWuqwmBx1cfKHedx1cfRE6KDDmBGS5SXBKOa+Gt8W40e/bswf79+7Fo0SL4fD78+OOPuPnmm3HXXXdh+vTpKCkpwdNPP427774b//nPf2J2azlw4ADeeOMNTJ8+HW63Gw888ADuvvtufPDBBzFn71i/fj1SU1Pxr3/9CyUlJbj77rsxdepUzJ8/H5qm4cYbb4Sqqpg7dy48Hg8ef/xxrFu3DgMGDIh5DPv378dVV12F888/H5MnT0ZZWRlmz56NK664AsuWLbOmUX333Xcxffp09OjRA7m5ufjqq6/w0Ucf4a677sLf//53pKSk4OOPP8bEiRMxYcIEPP7449i5cyceeOAB7NmzxxHCvPnmm5g7dy5UVUWXLl1q6NWg+sIQhIiIiIiIyCaoCvx16W5sPOCveOUEHfWpuHHZ3oTXP6GlF3MvbA+3UrNByPjx49GhQwcAwKZNm3Dffffh6quvtu4fN24c/vznP+Pw4cNo27Zt1PbBYBAPPPAAevbsCQC48cYbcfPNN+PgwYNo2bJl1PqhUAhPPPGE1a1m7NixmD59OgBg7dq12LBhA1asWGGFCzNnzsTQoUPjln/hwoVo2bIl7r//fmvZzJkzceqpp2LlypX44x//CADo2bMnRo0a5dg2Ozsbf/nLX6zbt9xyC0aMGIGbb74ZANClSxcIIfB///d/2LZtG7p27QoAGD16NHr16hW3TNS4MAQhIiIiIiKy2VMUrNEApCo2HvBjT1EQnXI8NbrfTp06Wdd79uyJ7OxszJ07F7/++it27NiBTZs2AQBUVY2zB1jhAABrLJJgMBhz3dzcXMe4IpmZmda6P/30E7Kzsx2tK1q0aIHOnTvHfeyffvoJ27ZtQ9++fR3L/X4/tm3bZt0+5phjoraNXLZ161acf/75jmVmC5QtW7ZYxxlrX9R4MQQhIiIiIiKyaZfpxgktvfUahJzQ0ot2me4a329KSop1fd26dbj++usxZMgQ9O/fH+effz7KysqslhHxeDzRwYwQsbsNxVrXpCgKNE1LsOQ6TdNw6qmn4h//+EfUffbBYe3HGW+ZECKqC48Z/rhcrrjbUePGEISIiIiIiMjGrUh4aXT7So0JMvmjfTjqi39C3yxFweMjWtXrmCCRXnrpJQwcOBDPPvustWzBggW1+ph2PXr0QFFRkaPrSX5+Pnbu3Bl3m+OOOw7Lly9HmzZtrIAlPz8fd999N6677jqceuqpCT9+t27d8M033+Caa66xln399dcAnK1dqGlhCEJERERERBRBkSX0yEu8BcDkM1ri7o/2lXN/Hvq1TauJotWYNm3a4OOPP8bXX3+N1q1bY82aNZg1axYAIBAI1PrjDxw4EH369MHf/vY3a7DSJ598EmVlZTEHWQWAq666Cm+++SbuuOMO3HzzzZAkCdOnT8dPP/2E4447rlKP/+c//xm33347nnvuOYwcORI7duzAQw89hKFDhzIEacI4RS4REREREVE1DeuSgWkjWqNluuJY3irdhWkjWmNYl4x6Kll8t9xyC/r06YObbroJF110Ed566y088sgjSElJwffff18nZZg1axZat26Na6+9Ftdccw169eqFtm3bwu2O3RWoQ4cOeO2111BWVoarrroKf/rTnyBJEubPn48WLVpU6rHPO+88PPnkk1i5ciVGjRqFf/zjHzj//PMxc+bMGjgyaqgkEa/zVhOkqiq+++479OnTB4qiVLxBkhFCoKCgANnZ2XGTV2raWAeSG19/Yh0g1gFKtjrg8/nw66+/onPnzjU27oOqCXy3rwyHSlXkpino0zq11ru11CQhBFRVhaIotV4Hjhw5gu+//x6DBw+2Qo9AIICBAwfiH//4By666KJafXyKrS7rQGVU9H5N9Hyf3WGIiIiIiIhqiCJLOLmBdXtpqFwuF26//XZcccUVuPLKKxEMBvHSSy/B4/HgzDPPrO/iURPF7jBERERERERU57KysjBnzhx89913uOiii3DZZZfh0KFDWLBgAZo3b17fxaMmii1BiIiIiIiIqF6ceuqpeOONN+q7GJRE2BKEiIiIiIiIiJJCgwlBjhw5ghEjRmDNmjUVrrt161acdNJJCa1LRERERERERAQ0kBDkm2++weWXX47ffvutwnXLysowadIk+Hy+OigZERERERERETUV9R6CLFq0CHfeeSduv/32hNafOnUqzj777FouFRERERERERE1NfU+MOrgwYMxatQoa3qk8ixevBg7d+7EI488gueff77KjymEgBCiyts3VebzwucmebEOJDe+/sQ6QKwDlGx1wDzOZDrmyuBzQg2pDlT0fk20rPUeguTl5SW03rZt2zBjxgwsXLgQiqJU6zELCwshy/XeCKbBEUKgtLQUACBJUj2XhuoD60By4+tPrAPEOkDJVgcCgQA0TYOqqlBVtb6L02BomlbfRaB61hDrgKqq0DQNRUVF8Pv9UfcnWuZ6D0ES4ff7cfvtt+Oee+5B27Ztq72/rKysagcpTZGZnGVnZyfFhx5FYx1Ibnz9iXWAWAco2eqAz+fD4cOHoSgKzw8i8PmghlYHFEWBLMvIzMxESkpK1P2JBpmNojnEDz/8gB07duDee+9F//790b9/fwDATTfdhAceeKDS+5Mkif/4j//4j//4j//4j//4j//4r8bOD7SDBxDatjXuP+3ggVo7hnHjxuGiiy6Ke/8//vEPDB8+vMJjXbRoEXr06GGdNw0fPhzPPvts3PWfffZZDB8+vFLP9eLFi3HkyBHH49V3HaiPfyUlJTj//POxf/9+SJKEdevWoUePHlH/vvzyy3L38+ijj6J///7o168fNm7cWOHjTpkyBePGjYt7/549e9CjRw+sXbu23PqyZ88eTJ06FcOHD8dJJ52E4cOH48EHH8SBAwes+3v27In58+fH3D4YDOKUU07B7NmzUVpaivPOOw+///57QnWoovsr0ihagvTv3x8bNmxwLOvevTvmzJmDgQMH1lOpiIiIiIiIAPXAfhy86SogGIi/ktuDvDn/htKyVY0//pgxY/C3v/0NP//8M4477jjHfYFAACtXrrROfCvj7bffhtfrrbFyrlu3DpMnT8Ynn3wCABg5ciTOOOOMGtt/YzJ9+nScc845aN26NQBgy5Yt6NixI/7973871svOzo67j82bN2PBggW4//77cdZZZ6Fly5a1WmbTt99+ixtuuAEDBgzAY489hnbt2mHnzp146qmncOWVV2LhwoVo3749Tj31VCxbtgzXXntt1D4+/vhjFBUV4Y9//CPS09Pxl7/8Bffddx9efvnlWi9/g24J0rdvXyxdurS+i0FERERERBSXVphffgACAMGAvl4tOPfcc5GZmYlly5ZF3ffJJ5+gqKgIl1xySaX327x5c6Snp9dEEQFED1yZkpKS8BiRTclvv/2GRYsWYdy4cdayrVu34rjjjkNeXp7jn8fjibufwsJCAMCZZ56Jdu3awe1213rZA4EAJk2ahNNOOw3PP/88Bg4ciPbt2+P000/HvHnzUFpaitmzZwPQw7kff/wR27dvj9rP4sWLcdppp6F9+/YAgIsuugibNm3CF198UevH0KBCkC1btjhadqxfvx4XXnhhQusSERERERHVFKGqCP6yBYGNGyr8F9z+c0L7DG7/ObH9/bIFohIDtaakpOCCCy7Ae++9FxU0LFmyBKeffjratGmDffv24c4778Rpp52GE044AUOGDMGMGTPiDig5bNgw64QWAN58802MGDECvXv3xvjx41FQUOBY/+eff8b48eMxcOBAnHjiiRgxYgTmz58PAFizZo110j98+HC8++67ePfdd9G9e3dr+/z8fEydOhVDhgxB7969ceWVV+Lrr7+27p89ezbGjh2LuXPn4swzz0SvXr0wbty4mCfZJiEE5s6da3XbGD16tOOH9jVr1qB79+6YO3cuBg4ciIsvvhi//fYbunfvjueffx6nn346hg0bhsLCwoTKd8UVV+COO+5Av379MHXq1JhleuWVVzBw4EA0b97cWrZlyxYce+yxcY8j0rvvvouxY8cCAM4++2zr+u+//44777wTp59+Ovr06YM///nP2LJlS9z9bN26FePGjUOfPn1w7rnn4quvvir3cT/77DPs3bsX48ePj2pZlJWVhblz52L8+PEAgHPOOQfZ2dl47733HOsdOnQI//vf/zBmzBhrmcvlwogRI/DSSy8l/BxUVaPoDkNERERERFRXRDCII5NvRnDrphrdb+HsaQmv6+7WE80ffw5Sgr/ujxkzBgsXLsQ333xjjaF4+PBhrF69Gk8//TQA4MYbb0SLFi3w0ksvISMjA5999hkefvhh9OrVC2effXa5+3///ffx4IMP4p577sFpp52Gjz76CDNmzECbNm0AAGVlZbjuuutw6qmn4t///jdcLhfeeecdPProozjllFPQt29fzJ49GxMnTsRbb72Fbt26Yfny5db+VVXF9ddfj2AwiGnTpiEvLw+vvfYarr32WixcuBC9evUCoP9Qnpqain/9618oKSnB3XffjalTp1phS6QZM2Zg2bJluP/++9G1a1esW7cODzzwAIqKinD11Vdb63322Wd48803UVZWZs0kunTpUsyfPx9lZWVIT0/HpZdemlD5evXqhSVLlsQdqPOTTz7BTTfdZN0WQuDnn39GXl4e/vjHP2L//v3o1q0bbr/9dvTu3TvmPkaOHImMjAzr+ezYsSOKi4tx5ZVXokOHDnjhhRfg8Xjw3HPP4U9/+hOWLFkSNclIUVERrr32WvTp0wdvvfUWDhw4gL///e/l1oMffvgBaWlpjvDKznweAMDj8WDUqFFYtmwZbrnlFmv5smXLkJGREVXnhg0bhgkTJqCkpKRGWyBFalAtQYiIiIiIiOqbuv/3Gg9AKiu4dRPU/b8nvP6JJ56IHj16OLrELFu2DFlZWRg2bBh8Ph9Gjx6Nhx56CD179kSHDh0wduxYtGzZstyWAqYFCxZg5MiRuPrqq9G5c2fccMMNGDp0qHV/WVkZxo0bhwceeABdu3bFMcccgwkTJgDQWzl4PB5rfIvmzZtHze7x+eefY+PGjXjqqadw6qmnomvXrrj//vvRrVs3R+uAUCiEJ554Aj169MDJJ5+MsWPH4ptvvolZ5tLSUsybNw933303hg4dio4dO+KSSy7BtddeG9Xi4Prrr0enTp3Qs2dPa9lVV12FY489Fr169Uq4fABwyy23oEOHDujUqVNUmX7//Xcr5DDt3r0bpaWlCAQCuP/++/H888+jefPm+NOf/oRffvkl5rGlpKQ4ns+cnBwsXboUR48exTPPPIPevXujR48eePLJJ5GSkoLXX389ah/vv/8+ysrKMG3aNBx33HE4/fTTcc8998R8PFNBQQEyMzMTHl9mzJgx+O233/D9999byxYvXozRo0dHdfXp3r07gsEgNm2q3fceW4IQERERERHZKK3awN2tZ70GIe5uPaG0alOpbcaMGYNnn30W9913H9xuNxYvXoyLLroIbrcbbrcbf/rTn7By5UrMnz8fO3fuxObNm3HgwIG43WHstm7divPPP9+xrG/fvti8eTMA/UT8qquuwvLly7F582bs3LnTOplNdP+ZmZmOcECSJPTv3x+rV6+2luXm5iInJ8e6nZmZiWAwGHOfv/zyC/x+P+6++25MmTLFWh4KhRAIBODz+axlsQKLY445ptLla9GiBTIzM+Me58GDBwHA0RWmQ4cO+Prrr5GWlmZNSzt9+nRccMEFePXVV3HjjTdGPffr16+P2vfWrVvRqVMnx769Xi969+4dM+gy17eXt2/fvnHLDgDNmjVDQUEBhBAJBSE9e/bECSecgGXLluGkk07C5s2bsXnzZkyfPj1qXbPc5nNUWxiCEBERERER2UhuN5o/8QJCv/4C4fdXuH5oz28JdXXJmng3XO06Vvz4Xi9cnY+FZJwQJ2rUqFF44okn8N///hcdOnTApk2b8NRTTwHQW2pcffXVKCsrw3nnnYfRo0fj73//u6NLSEUixxuxD8R56NAhXHbZZWjWrBmGDx+OQYMGoVevXhgyZEjC+451Uq1pGlyu8GlreQOFxivvzJkz0aVLl6j77fuKNQuOvbVKouWLbOESydxH5HMZGZzIsoxjjz0W+/fvR8uWLbF48eJy91teGVVVdZQxchu7eOuZ+vXrhzlz5mDz5s2OVjOml19+GTt27MCDDz5oLbvkkkvw3HPPYfLkyVi8eDFOOukkR5hkMsMypZL1vrIYghAREREREUWQFAXuY2OPexC1boLTyLq7HJfwPqsiJycHI0aMwMqVK9G6dWv069cPXbt2BQCsXr0aGzduxP/+9z/k5uYC0AciPXz4cNSJcCw9e/bEN998g2uuucZa9sMPP1jXly1bhvz8fHzwwQdWOGK2PjD3X17Lge7du6OwsBBbt251nCB/8803lRow1K5Lly5wuVzYu3evo+vOggUL8MsvvzhO1CtSU+Vr1UqfIvnIkSPWa/PZZ5/htttuw4oVK6wxVkKhEDZv3oxzzjkHLpfL0Solnm7dumHx4sU4fPgwWrRoAQDw+/348ccfcdFFF0Wt37NnT7zzzjs4cuSI1QrD/prGMmjQILRv3x4vvPACZs2a5bjvyJEjePnllzF48GDH8lGjRmHatGlYs2YN3n//fcf4IHaHDx8GgFqfMYhjghAREREREVWDnJUDuCtooeD26OvVsjFjxuCzzz7DypUrHbNvtG7dGoA+2OeePXvw9ddfY/z48QgGgwgEKpjeF8ANN9yAjz76CC+++CJ27NiBV199FR988IFj/2VlZVixYgX27t2Lzz//HHfccQcAWPtPS0sDAGzevBklJSWO/Z9++uno3r07Jk2ahDVr1mDbtm2YOnUqtm7d6gheKiMzMxNXXHEFZs6cicWLF2PXrl1YtGgRpk+fbgVBiaqp8rVs2RJt27bFxo0brWX9+/dHixYt8Le//Q0bN27Eli1bcPfddyM/Px/XXnttwvseNWoUsrKycNttt2HDhg3YvHkz7rrrLpSWluLyyy+PWv/8889HixYtMGnSJGzevBlr167Fo48+Wu5jeDwePPLII/jvf/+L8ePHY926ddi1axc++eQTXHPNNUhJScGkSZMc22RlZeGcc87B008/jeLiYpx33nkx9/3TTz/B6/WiR48eCR9zVbAlCBERERERUTUoLVshb86/oRXmx11HzsqB0rJVrZdl0KBByMzMxOHDhx0nm71798aUKVMwb948zJw5E61atcLIkSPRpk0bx6CV8Zx11ll46qmnMHv2bDzzzDPo06cPrr/+emv60z/84Q/YuHEjpk2bhuLiYrRr1w6XXnopPvnkE2zYsAFXXnklunXrhiFDhuC2227DHXfc4Rjbw+Vy4ZVXXsG0adMwceJEBAIBnHDCCZg3bx769OlT5edjypQpaN68OWbNmoUDBw6gdevWmDBhAm644YZK7acmyzd8+HB89dVXVsCRkZGBefPmYfr06bj++uvh9/tx8skn47XXXqtUWJOVlYXXXnsN06ZNs/Z98sknY+HChejQoUPU+mlpaViwYAEefPBBXHnllcjOzsatt96KyZMnl/s4p556Kt544w3861//wqRJk3D06FG0atUKZ511Fm666aaYZR4zZgyuueYaXHLJJcjIyIi536+++gqnnXYaUlNTEz7mqpBEIm2fmghVVfHdd9+hT58+td7PqDESQqCgoADZ2dkJj/ZLTQvrQHLj60+sA8Q6QMlWB3w+H3799Vd07ty5wrEckoUQAqqqQlGUpKgD9eHXX3/FhRdeiE8//bTWu35URX3UAb/fjzPPPBMzZ87EoEGDYq5T0fs10fN9dochIiIiIiIiqiOdO3fGhRdeiNdee62+i9JgLFq0CD169IgbgNQkhiBEREREREREdWjy5Mn44IMP8Pvvv9d3UepdSUkJXn75ZTz88MN18ngcE4SIiIiIiIioDmVmZmLlypX1XYwGIT09HR9++GGdPR5bghARERERERFRUmAIQkRERERERERJgSEIEREREREltSSaMJOo0aqp9ylDECIiIiIiSkputxuSJKGkpKS+i0JEFSgtLQWgv2+rgwOjEhERERFRUlIUBdnZ2Th48CD8fj+ysrLgcrkgSVJ9F63eCCGgqioURUnq5yGZNbQ6IIRAaWkpDhw4gJycHCiKUq39MQQhIiIiIqKk1bp1a6SmpuLAgQMoLCys7+I0CJqmQZbZaSCZNcQ6kJOTg9atW1d7PwxBiIiIiIgoaUmShJycHGRnZ0NVVYRCofouUr0SQqCoqAiZmZkNohUA1b2GWAfcbne1W4CYGIIQEREREVHSkyQJLpcLLldynyIJIeD3+5GSktJgToCpbjX1OtCw2rcQEREREREREdUShiBERERERERElBQYghARERERERFRUmAIQkRERERERERJgSEIERERERERESUFhiBERERERERElBQYghARERERERFRUmAIQkRERERERERJgSEIERERERERESUFhiBERERERERElBQYghARERERERFRUmAIQkRERERERERJgSEIERERERERESUFhiBERERERERElBQYghARERERERFRUmAIQkRERERERERJgSEIERERERERESUFhiBERERERERElBQYghARERERERFRUmAIQkRERERERERJgSEIERERERERESUFhiBERERERERElBQYghARERERERFRUmAIQkRERERERERJgSEIERERERERESUFhiBERERERERElBQYghARERERERFRUmAIQkRERERERERJgSEIERERERERESUFhiBERERERERElBQYghARERERERFRUmAIQkRERERERERJgSEIERERERERESUFhiBERERERERElBQYghARERERERFRUmAIQkRERERERERJgSEIERERERERESUFhiBERERERERElBQaTAhy5MgRjBgxAmvWrIm7zsKFC3Huueeib9++OPfcc/H666/XYQmJiIiIiIiIqDFz1XcBAOCbb77B5MmT8dtvv8Vd5+OPP8bTTz+NuXPn4qSTTsJ3332HG264Abm5uTj33HPrsLRERERERERE1BjVe0uQRYsW4c4778Ttt99e7nr79+/HX//6V/Tp0weSJKFv374YOHAg1q1bV0clJSIiIiIiIqLGrN5bggwePBijRo2Cy+UqNwi5+uqrHbcPHz6MdevWYcqUKZV+TCEEhBCV3q6pM58XPjfJi3UgufH1J9YBYh0g1gFiHaDGWgcSLW+9hyB5eXmV3ubgwYO48cYbceKJJ+KCCy6o9PaFhYWQ5XpvBNPgCCFQWloKAJAkqZ5LQ/WBdSC58fUn1gFiHSDWAWIdoMZaBzRNS2i9eg9BKuu7777Drbfeiv79++Oxxx6Dy1X5Q8jKyoKiKLVQusbNTM6ys7MbVWWnmsM6kNz4+hPrALEOEOsAsQ5QY60DqqomtF6jCkHefvttPPzww7jllltw/fXXV3k/kiQ1qhezLpnPDZ+f5MU6kNz4+hPrALEOEOsAsQ5QY6wDiZa10YQgH3zwAR544AG88MILOOOMM+q7OERERERERETUyDTogTH69u2LpUuXAgCeffZZqKqKW265BX379rX+3X///fVcSiIiIiIiIiJqDBpUS5AtW7Y4bq9fv966vmzZsrouDhERERERERE1IQ26JQgRERERERERUU1hCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUGIIQERERERERUVJgCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUGIIQERERERERUVJgCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUGIIQERERERERUVJgCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUGIIQERERERERUVJgCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUGIIQERERERERUVJgCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUGIIQERERERERUVJgCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUGIIQERERERERUVJgCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUGIIQERERERERUVJgCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUGIIQERERERERUVJgCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUGIIQERERERERUVJgCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUGIIQERERERERUVJgCEJERERERERESYEhCBERERERERElBYYgRERERERERJQUXPVdAGr8hBCAqgKhEIRmXKoqoKkQoRCgqvptWYIkK4AkQZJlABIgS4AkAxIASYYkS8ZyGZCMS2M9ybYebOtJklSfh09ERERERESNBEMQiiKEADQVCBnhhWqEGrbrIhgAAn5owSAQDOqBh6rp6wgN0DQISJAkAELoYYYQ+j8AkCT9cSRAMhbByjIkPQAxL2XnbSsokaRwUCLJgCJDkhRIigTICqAo4eVG+ALJCFNk2/axwhdjOWQJEuxlsKtm+FIT4U2ld2E+h2wERkREREREyYchSJKIF2YgpEJoKkQwCOH3QT16FAGPG5IQeqsOVQU0TQ82BCBgnHcrit4KwwwbFBdkt2JbXvWTbGGGJVZoIgAtfN26X9P0y1AoXD4hoBnX9e216PBFfxQjfJH0/UvG9XLCl6qEFtXPOWohaDGXyTKgKPprJemvmZAkqKVlCBZkQXIpEeGRLXQygyEYoZRkD4/CrXucgZOtJY+5v3okzPpjVmyrrgDCdt1aDoTrHASEZm5bzvbGNkJogGq8jzShPx+KAsn+fpGM8E6WrNcEsmwFc5IR6FnPn6LUx9NGRERERNSoMQRppISm2YIMo8uJrSuKUEOA36+HG8FAuEuKpoZPxoQIhxqyDCHJgM8HuDIBlwuy26OfdMlKnbYckCoIHGrr1FlEBi/CGb5A2Na136jag1Vv++o8rvlPVYFgwDiZBzSh6fWqpARqSaHeAkZo4U2ta7YWPvpNRAVIEsJhidnVyQxAgHCYYp7021vwmCf7Zr0z6p4VFBiFqTBoEHqAJ4zXUGiqsVxvqeR4PiCMCy18oPYgxKwP5jZWIihZy4UZqAHhgMd6jswAyHh+zMcyyyFJEGYwJ5n7sYVLtqBJsrdWUlyAIkNWXEYYqQeSVrhihlBW0GU+x5IVsEhW2CIZx0BERERE1HQxBGkEhKYhtP93wOeDCAb0UCMU0k9YHa01BIQE/eQVUrgbiHFCJHu9xkmnHPNXZCEEpOJiyBkZ9f4rfX2oKHxxrFvLZakvQghIbi+UStaBuAGSraWEMAMVTQv/E4HYLXgQsT0Q7kIF82Y4gNAXRAQNRgAj2YOXyCDGatkCyOb29pY/5v4Qrht1FQhGtXgywh0hbM+f6gc0Dar1/GvhoAdWJAPJHtrZQxTruj62jt4SqBSBrGxILiNMMYIWK1yxBzH259+R64VvCMc6ccI/R8BkbmMui7zfSqkcV8P7FhHXEb2tCJdLso9LZAZPsuS8Hdkayv7ecLRqstcZwF4Pnbel8E3Y1o2xb8TZt2Tbj6OeWtvBtp39MZvqXy8iIiKixDAEaQxCQai/7wYCAUhuj/ULrmxeN38155dbqieJBEisnZXj7H5kW17N/YqIUAVChFvVGOP6CL8P8JnBlD2IEeEwyixInFzD8ffIFmTFvO7cMPbRSrHWiXN/AsslR6hmb+lla/1jlj3ytr0cjufEvG08loAtgAq3FAofg61FUazjtoIY2zb2sMNxSGZQ47ztfDyDWaesgAeOMEctKUEwM1MPEB3hjNkqyRbmmN3iqhoQOY4hjoQ/2xJcL9GwO+E3mxRxXDE2Lu+47cGWY137fmLcF7mcQReRRUR+vlR02/FhFv57xvcTUdPEEKQRkTOzIHlT6rsYRNSIhVuyhFuDWXmGEJCESNrWYPXJavkTXoDY4Uz8+0SsoMbRSgvhUCvGfoQwxlwqKYGmqhG5TeTjxbgdS6yASJgn/bG2sde7OPu07TvqeGOul9juHPuscOWIx44ZfsQLLGyPFSvQi9zG0XLIvp84IVfkvu2hlS1YcbSSgxm86vVALTaCMHPQcPt+rP3bi2MPuyLXsZ1IllfmGMulcp/PcvZhqqhuVFgnyl8h6kS7so9f2e1iLHe0nHPcEWN9e/AbtTIcfwvUomIEj2bEiRbjveeFc9dxWuCFH8uxcvyWg1HHEuPxE2l1WNF9do56LAG2Mc4kq3uqFA6TZQWQAUB2jvVl249kbx1oD5et96UtzJait49cX4p6X9tuhw+k/OOLs8z8PBBC8PsANUkMQahWqEcPQxQXx71fysiA0qxFHZaIiKjhqkx3vLj7qIFyCCEgKS4GYVUQFWTpC80rtgvnCWci68QMuMzbMQO0yDJEP6bzXNW+vQaUlEILBRAzlHLsvpyT1orWMcVqIRZR90Tk/eZ1R0urmA/uvK+csK68+xN+L1Rx/wkHepHrR99RqcXx9iMEgNISqKXFzmOPuXqsk+nyF5T7fEYFeuXsq6LXpTL3R7ZetI055lhmvHes8Ml8v1qXEdcjHs++RIr5ftA3jQpJJdtlZCDiaAlYyc+SuHVAQCspQSA9PXaIWc62jsA0ct1Ym8TYjxRrH+F74+4r4foat+zlbBwvtI68P+YxxrojxmsVFWLp68V8zyS6LJa4z3mYEAIiGAKysxPbZyPDEIRqnHr0MPIfvVcfsDUelws59zzSoIOQphDkNIVjICJqDGoiyIq771rZa2xCCEguD4OwJCaEgOSp/PhgVHMcoWqswCXGMhEZvCTSoqi8dcyWg9UKAePtv/ztK9uGqsJWWeXtsZotwsq/O1YrrTgttxLdcSJhqT0wrmhZHFowCC2rGdCufcWP1wg1mBDkyJEjuPzyy/Hwww9j4MCBMddZtWoVnnzySezatQtt2rTB3/72NwwdOrSOS0oVEcXF5QcggD6wa3Ex0EBPwJtCkNMUjoGIiIgo2VQlVK3JuErvHguGoUlMHDmMysdRjUeDCEG++eYbTJ48Gb/99lvcdXbs2IGJEyfi6aefxllnnYUPP/wQt912Gz788EO0atWqDktLJiEEhN8HUVwMraQIorgIWkkx1N3xX0e7khWLoWRlAy4XJMWlX7rcgEvRLxUXJJe53LhUbPebyx3ruQFFCV9WcSaPphDkNIVjAJpGa5amcAxERERERE1BvYcgixYtwqxZs3DXXXfh9ttvL3e9/v374+yzzwYAjBw5Eu+++y7efPNN3HLLLXVV3DqjHtgPrTAfACBCQYT27obs8eqzw6B2TpqEpkGUFEMtLoQoKYZWUmwEG0VRQYcoLoZWXASoFZxklyO06QdUfesEGVN8RockLsDljg5RjDBG+H0J7d73v88gZ2XpNyKamAlHM0bEXCdmX/AE1hH29R33hZdrxUUJHYP/+68R+n23XrdcLmghFcGsLH32IbcbktsDye22rsPlqrNfBZpCa5amcgyNPcThMVBN4GvQMPB1oJqmCuC7o8AhP9DCA5zUTECxhuBoHC0hVAF8nw8c8kvI9QqclAMojaPoRHWu3kOQwYMHY9SoUXC5XOWGIL/88gu6devmWHbsscdi8+bNtV3EOqce2I+DN10FBAPxV0rgpEkEg9CKbcFFSZF+u6Q4vLzYWF5SDFFaAgiBglo4pnqjqvqUnwE/gJpv1OVfs7qG91j3fJ+siFpWYXzidhvBiAeSK8Z1jx6cSC53zCBFcrv1EMoKWmLvTz24v05as8QPrIQzaBIV3xfZJ1c9dLBRt8hpKiEOj6FhaMwnr8nyGiA9HXB7665AlZQsr0NDfi+YGvoxaEJA0wBVCKiagCr0Zaox1IUqgNDhQ9jweymWHXChKBRODDJdAufmBtEjLxVadnMAzolazEt7e2PJGKhU1m9AkqyJr61l5oVjfFOY64ZXiByC0zFMaMR4riL/CDbsLcG7e1zIt70tclzAZR1C6NcuA67mLSBLEmTAmOimYaUjDb0uJaKpHYNaWADh8SCYnmbVTTkrB0rLptEDo95DkLy8vITWKykpQWpqqmNZSkoKSktLK/2Y1jSADZRaeLT8AAQAQiH4vvwvJI/XGWpYYUcxYJz4N1RSsxaQZBkiFNRPAkMhvWVJRSeMVP+CQYhgEEBpg+gtWDDrMf1T3d5qJiqgSKBlTj0qeP5JSF5vjK5eZhcwF6C4I7qIRdxW3JBciq3VU0QrKFd0qyf7foSiQNM0x99HrbgooRBHKy6CnNO8lp+lquExJM4+LWJNU48eRsFj91V48po95eEG+UWxKdSjRF8D18QpEOnpdVewSkim16G+3guJ/B2o62MQQkATZqBhCzOMQCNkBB1BTSCoCoQ0IKQJCOjrC+jr2edFUgqPot1LD+MUNYRT4h2n4sKhG/4OLbt59OzjMcpouxXdsBcRX0FiH2kFz4STUngE7V56BL3VEHrHWadEcWH/X++DyG4O2ZhFRpb0IESRAJcsQZYBlyRBkSUr3CnzqQgpIciypK8PPaxRZP1Sv139QKWhvx8S0ZSP4Yj9htuD3DmvQ8lruEFIot9f6j0ESVRqaip8PmcXBZ/Ph/QqfEgXFhZCruJYEXVBK+8XGhvfR+/XckkMkgSkpgHpGZDS0sOXaRmQ0tMjlmdAFBxF6MVZFe5WufwayG07RC0XQhgtOEL6pS0cEebykPHPuG0FKOb61u0QRMi2jWruS4WwL4vcl98HFBVW/NwoLv2TJGq6rhi3o+4rZ6qxiqYhkxJ4TFUFEuwS0+g1heDMVwbhK2sQodJRRQ9SoLgSHpit8LUXre56EFqF0wbqVyNHv09sWyS6rdliR0vsWS18/in9uGUFkGXrnyTLxk9nEf9iLTPXL+f+yOWSrBjfJGXjsaWoMoj8owkdQ8mWnyAdOuSYNhFABZeSNS2fABAIBCC83jjTMIYvpUrtHxD79yV08lqy7WdILYuiXmNhvr7WchG+Lmz1RERPaRm9jbFeZH2ypsCM3qc4eiSytDEVf7sW0q/bwgsinwsg5vOpX7Mvi/GZEGuqysjXKOb+9eXicGIt0/ybf4QoOFozAy3WcKAmDh1MaL2Sn7dCOhrxvqnG9JGOMtiuaPbbAISQoBlLwn+OJMd60qH98CbwOuzfsAHIa2O8xFL4ZNO8LkuQjaYHEvTbEvQTVkCCJOv/rLogyc7jizPVqoCA3++HCAYgSXJkkwV9nYMHEns/HzwAOUbLImeIYQQc0K+by4NGkBFU9fBDgx5kaEYgEqtmycbxy7Le0qNEU1CmSSjVZJSoEko0GSWqjGJVQvqBAG6ooFu3oobwzEYVu5qnQYKwwgD9UliBQcxlxu2421iXIhwowLmNFLGObLxU5u0Wh3zomMAx7D9UiKCcBUXS4JYFXBBQjHIL4/Wwfi+S9LAo6A/CXRCyugOZ5TGDD3M2VwmAIklwyXr5FCMoUST7+rDqZjg8MQKU/dWrSw2BVs33Q0OQ0DEEAyjcuxuyJ6VuClUFmjmtdQUaTQjSrVs3bNy40bHsl19+wYknnljpfWVlZUFRlJoqWo0LZmQgsa9aVeRyQc7IhJSeASk9Ux/5OV2/HXC5kdqiBeSMrPA6aemVGmA0JFQkEB8gLS0NroyMqh9HLQrt3onCpx+ucL2sWyfD1f6YOihR5SV6DBn/NwmuvFYQwSBEMICSwgKkulyA2UInGLBafljXQ0FjWUBvERJxn74sYFsvfH9Dan1BcRjdyIBKtCY7sK9BBDjV4iuLubiyx1Wfz4P6/rvV3ocC1P54TeUI/fulenz06tM++7C+i1BtyopFUOu7ENWkLvt/9fK4EvT3UE1IWfLvSm8jgBp57Wrq70DoxdkQimKFLUICAFm/NJdBb1ngstaxhy4yzL4jGpz/VEmGKgAVkvFPhiokhACEIEMD4IUMjwRkQdZP9o3H0CQJqaHExn+74sc3UeJON8puBIrWscA6BiGF73NcSuY6ztv2/Zj3hZdVcNu4nhVI7Meuo19+iaPeHFtZjJJLkhFc6IGE2epDHw9FwCXrrUPsLUfM6+HtzEBHCl+XoAch0C/NcESyhW6yntbBVXQUmQkcQ8mPG4Hdv1u3bbXHyPhs4aUQzqwYgGT/cLbyYucnthR1JRz0WN9fzZAx/GBAfoJnbhs3AHt2RX8Xjhj/z9moKP66Mdezp7S25VHjCUaOZWiMRVmRjIwMuLOzE1q3PqhqYn8BG00IcuGFF+KVV17B8uXLcc455+DDDz/E2rVrce+991Z6X5L1JmyY7G+rhNZPSYWUkQE5PRNSRibk9Az90ggx9OUZkDMyIadnAl5vzOMXQkArLoa3mtNhyRmZ+q+pFTQJkzMyG+zrkOhrIKHh1qVEj0FJTYPSTG8yLISAkpUDTy1NiWa28hHBABCyhyP2sCUcrIQO/A7fx8sr3K934GDIWdmw/6Ia/cto5G39f47jjLxe3n1RjyXZVgnfpx49EnPclUiefgMhpabpz0vIbPkUct62dx0zb6uqcZsBExERNTChIKRQ0LrZML8xla/3kS31XYRqG7bny/ouQrV5P/+gxvcZ1aWpxh/BKbT641p+hNqnCTTYcx8g8bI16BCkb9++mDp1Ki688EJ07doVzz33HJ588knce++9aNeuHWbPno3OnTvXdzHrTeYNt8F9XA+9T38DojRrgZx7HmnUgwNJGRkJBTlSA23JAjTMY5AkKTwORQKUXTsTCkFSTh8KV4cG2iJn186EQpDUs86p9jEIozuYFZaoccKTGMuFcTtQUgK3IlvdytSCowh+/02Fj+3qcpzeLc4cCM4eFMVpcu1YV/8ZR99ZOdtKtm2tdSNvA+HWa5IErbAwoUGMPX1PgZyeoXe7UDW9u4SqQmjmdQ1CU4322Po/oWmAbZl+v7CWCce65W9LRERE1JBtPexH7+PquxTV16DOnrdscSat69evd9w+44wzcMYZZ9RlkRo0OSOzwQUgJqVZiwY500WimkKQ0xSOgSpHUhR9Cmhv1fqbCiGgFhcjzdYSKLRrJwoSCEHSL7qiQQdRiYQgqUPPrfFjUFWBkBAIaeY/IKQBgZAGv6ohYPR1V1UNqqqPeyFpGiShQoGAGxpkAXgP7kLWGy9U+HilF10DLbeVNRiBFDVuijkuBiIujbEwBOD3+5Hi8cAYDMNoQhxjm3j7iXo8/VLKP4TU/7xX4TEcGXIhQjl50Iwm4OHm4jI0yRxbQW9Or98v6+sa62v2S2NbzdrWWGY0hxeSBE1I4ftsTe7D9+nN6NMP78VZK2ZXWP5vL7wFwVZt4YaAWwJckga3ZF4XcEsCLglwGWMD2Js2Rz7XmvGamOPoCM0cr8R8yYzBK837BayxU0SMsXfcR/Yjb2XFXSz2Dr0EaNUOehKpNzeXHTmjZLSqdf6yL9tv23NMY6m5vgYJIWPMh6Cmd98IakBISAgK41Izr+vvmaAAgpqE9KP7MOzz+RUew8v9rsPezNZQNb17hDk7iL3bQWRTeONliBJrvapuK0Ggbcl+3PzDgooOAf88/irsS29p7UMWmtUsXzLe3+EuAXr9kaG//mbnC8ncxlhfNuqP+U7Q9yus++3bmPdLjsfT12lRdgQX7Py0wmP4qP3pKPBm27YV0fuzHUf0YwmrfOFywnafBrcEuM33GYT+PjMuXZKACxpc0N+LLsD4iwHAVwbXvl0VHkMotzUkj8fZzcBx3fYaR802Z/+bifB12/ZSrO2t65GP5dyXCIWg+EoqPIag22v80BCvPOHX2v5Y9roDwOhURA1N+F1uiOqaFbkcjvsBCZLQ4BEVd4LLL2saP9o0zDNoogagsQc5QOM/hobYmqWy6uIYhO0ECMal/tVJRH3Xse4z+4javl9pQqA4oEH1qYCk36f5E+tbWRLUoPhV/SRICvcfbqjT8dUETYSDDdUWcgRVDf6QgN+YoUAVQh/Uz/reIKDIEhTjeXLLElJcijGIXOznSfYVJFamvNbQ2nR0LBNCHyDQPKEMarYTUOuEE9ayYl8QituDEJzrhgQQFJLjhDRk7lcz7rOva5x8mo/RTt2Jx1FxCPKg1hfb1E4JHW9d6lrsw1kJrLfgaDNs0xIbOd8MRNwy4JYFPMZ1lyT0ZRLgkW3rGLfdEuAytzGWu431Yt/W95fuSSwgPZp3DPxtj0NQAAENCAgJASOwCGgSAuZyTa87+m1znfD9QWOZ8359DIeq6iqlYFgC621I74htmZ2q/Di1qcSdltB6m1och23ZnWq3MFXUtWBHQiHI+53PTvgYZAhkuIAMl0CmSyBT0W9nGrfDy8PLUo2xpCuiagJ+AGVCb6ynagKufbuQN++JCrfdf95YBFq1hxQRaUWGgOYy63rkjiTJdsrpvCLFWt+xqRS1T0kCXPt2ofnL0yo8htJxt0FrE56MQLI/bswu8kBpWRnSUlNjjhOsacL4GyCM97ZAQDX+RqjC+ozwawJBVdJn7RECAVVfN2RsFxRA6oFduPk/T1Z4DPefeid2ZHfUP9OcI3LEJGI9o7GOtVLbx163U8EuPPHlY3H2FHbnafdie/YxzseNF0xI0ccY676a0rVgB2aunlrhejmpDXdykcpgCNIAyVk5gNtT/jS5DfzEj6gm1GVrlsggwfyxJDJMsAcJ1iwncIYJ1jYCQEo2pDsehCgpdnzSCvu/1AwcdGdBFAX04MH2Q41qlE0T4eBCMx9PwBZ4OH/cgVFmc5kG5wrWNo7jFfD5Qkjx+WF+0itBD9orLsjljD6vKS5s97uhHfY7RoCXbNfN0fr1UePNafkAWZbDo+Qb21jhiRQefE2SAAVSlQKVqgZRCbfi0MJTMMIotzXtoCTBrUjWcVeFEMBRVwY8shseLRh3vYDsxtRdLXDwUEpUIBHzy1xctTPqe4tG/gNioScTgQReg0JPIkP86YJGywf9h7XaDwq7FngxM4H15v6eiW2lDXP0/9p4HSrLJYUDJjOwcknO8MktO0Muly3QStESC5f7ZoXQq3VQH/zT8U9vERUyPi/MS9V432vmegivH7kPzVwXzuWV+1tRsQyXQPcMNX6g4QIylcQDDbN1lGp8/vlD4dliwpfm2gLm+0qWjYE7zUtJgivBv8mtMt1Ajtf2g4KwtbRyfg6bD+v4XAaMVlz2hiDhP4jmZzpiXMK+D/s+jStSgj/KlwU1BANanM/+WAR8PhU+hBD/b5P+/EoQSJGAVOOsMhywCPMKJOO/8PUwJSWxk+rbj1egtZWN1lDhumtdCr3Vn1XH4XxvRL2PYLyXHO8h27qI856J8f7yisROqY/JlNEqz/xeJKyZfxQpxoxCthmBzPvNWXUUYzvnzEP2QWoBRQq3sLLfb7bQk233SQBS9yU2UHC3Fg1zdpvKYgjSACktWyFvzr+hGaP0ilAQgV+2QPZ4rSko2Y2BKiNg/DKtVjBVqABQ4tcQcjl/p6uJc5cq7yMlW/9n7ifig1sAECUh40uI8cXEFiQIYX7BELbQQEQFCWYZHV8OEPmlxZ4jhMMELaJM0TKAVOMEWwKkqPUEUKB/oXc2Obf/WiRZvxTF/vVJDw6sITOsLxuR+5NiLNO3FwIohYK0VFf4R4a0PJRM+Aek0vhBlEjLQE52c+vLqf25NV+HkKa3hDCb74dfH+NLqgTHk2KfUi/qOgBF0UMRRdEDFX06PjkiTDGm8UvLgffOhyCVFoen64M+jaWqGV9iUtNxxJONYGGgCq04pHJbcSSiTAUO+iUcCMjGpYSDftm4lBAQ7ZE39LFyZwIo9GTioKsFEP/csF41hJPX6jiY2gI3JfIapDbcz+bG/hq4JIHC9OYJvQ6tW+aglzdodEMKt7DxmIGFGVZI0a1vyrvPJVX/R1iR4U3odbioawqknLp9Q2tCD3uLysrg9abqJ5hwniyqAtj9uxeB/1V8DH/s4sWx7aJnGjM/BzTjMYUAyoL6Z64ecAhrucX4qDD/3irGB6JL1qdndSmK/rop4c8CxQjfrdaJMuAywhDN3Rz5CQTkubnZUNLr55TJarVp3bbfCWhKMxQmcAyd2jWH1Cylwh9v7D/2FJcEkZZmO+GNCHfCIZCABmHNUl/edy/zO1N4W0BKz4CquKCU82OLqrhQ5k2DGrAeOU6go1lTFgOAVXoJ+pevCgIdwPkdq7zvYlaXPwBSmiuh9/SlXVzQsn0xvlfajsn+ndT2fdRedgnQZ/oR5R2Trfy2slp7sC+TAFdWWoWvg+Zyw53dLO79jYkkhOPt1KSpqorvvvsOffr0adBT5EYSAT/8P34H2ZsCyVt7v8wIIVBcXIyMWpoZhOqGqgr4VA0BVaAspKE4oAcgAVX/ICo/3BAo8/mQmpKCeH9Uqx2ORO620n+CKvhFogofXuG7nQFBeDtELXduF36cxvzeqaj5a10yAxXrC5Pjuoh7HZIUVTHDIQqsrjr6b1cVt+LQv0Cjyq04TCEBHAlIOGAEHAf8Mg4aAceBgIyiUMOvNzKcv3i7zG4ZkvPXb/1+oXfXsN1fGJSw7feCCk9eT+2UiQ6pwnqfmb9gWe8949cx8z4zGJRjXRrrO/Zju9++vQRzWxG1L/PVueunFBwNRv4FMAnkuAT+dqwfGsJdRcxm4fql0foj5vLY6wW0cJclc52ACI+TUdlf7vPKDtdIkCNBDxQ8MuAxwgW3rLd28EhGNxyju45+W4SXSdHremRhLAvvz1purCNL+nv91h9TcKSc16G5W+CZE30JdZOoSZqtm6FeEufJqy0/xw+7CrB0R/wWv6OOceP49tmOZbE+Xyo6xMhNEn1KSkvLkJaW6vj8s9ME8Ng3pRCl8cejkFLTcdtJ+vfW8Am38VkN6C37bK0zFElvseGSJXhkCS4FUGRZ/9VbNloTQp/G1ZrStZp/n9Wjhxv92Gm1cQx1eU4ghIB69DDW7SrBv3+TkW/LEZq5gcs6qDi5XTrkZi2sYMAMDhIJdBzhjSMwiRPo2H6oMVucCABCE1awA0S3yt24uwBLynlPj+7kwYkdsqO/kwCQZMn6DDLfc9YPOZIzsLB/jwXsn43277fOrleO78PW96DwCvo+JYijh/HN7hL8e5fzdWie4sKfTsrBwJ5toLRMrLtnfUn0fJ8tQajahBo5M4I+44FQNeu6vqLxlydy5gc43rXQT2Qj3tnmz9vW9uZ94etSjP1E/EWI2k9jPmEF9C9cAVXAH9LgU4Fiv4qyoIaQqiGk6V9S3YoEjyIhzWt0IzBn4IghZksAIoMmgM3FMvKDEnLcAj0ytFo7yZBjNXexVO5B7YFKSAO2lMjID+jH0DNTg1uufnglBFAYAg4EZBzw6+HGQfN6QMLhgGQNXFabemeqyPVqCf367YrRXN8lCYT8PmSmpsCtCMd+lGoWXxPArSXNsC3YHOWdvF7Wtu5PXhN1TfsgZv5qDhprL6T++XZthyCOSROOZbXJ7BYQELaBRTXzdrhLlHl7R6mEpftbVBhyXJRbgu7ZCryKM4Tw2EINxfZxWpckCPypfQCzfvUi3utwZVu9RZe5xP57n/0kKt6JlPV1BRGvomTeKUXdF3lSItvuifwqIkFCz3bZKMtU8ObvHhwNhtdu7tZwRZsAemWHu8xE1iQR43pUbUuoGoqYt4TQuwL6VS36b6Ntk+FdMvDPXbnGrejXYWInP5qnCat1hhly6M37wwGGJIVbZ9T197LGPnYa0PiPQZIkuJrnYlDzXJzSG/g+Hzjkl5DrFTgpp/qfPXWlZ8sUtGgHzPxZb9FpaukVuPU4DUMbdnagy83FoNxcnHIS8O1vhdgLLzr1OBZ926RW+8eghoYhCDkIVYUIBgFhn9oxItAQAkKCMYo9AFnR2xvKMiRZ0T/hUlKhuN2AywO43fqsFUZUKoRm/GyrIdxe3rguNAjNtl7U/ebPK8Z1Tc9nhfkrsKM/gi2etUfBiNhH1Lccc2vjIK3sRnJ8kdJ/cY64Hf492bFPyfriFGM7ewHMD/8Y38AEjC+zIQ1BVaAsJFAaVBFUgZCxvksCXIqMVJcExf6FxLxqHn+MYzYPVfH5IPlSIVmDOphBlfOfkOSoZdH/msbgSQSsO6pgwW43jkR8WR/XPogBzRLr315fzEClusfgUxHVTeWgrVWHX6u5LwhpikBLj0CeV0NLj0ALj8Ci390oUoHyAoS7jvVXK0DQg1ANaV5R4ye4sgSMqyBEGNc+WO0ARESc1NofIepXQ9vfV2cT5OiTQwGgR5qGGzpo+H+/e5Fva73TzCVwaRs/eqSpKArYT4B19lZm1vHZcj5Ha7JYy+K8GJIRUrkAvZN4zNPjsAE5wOdHXBW2ovhDCx8y0ipuERY5lpJmLXcGCvbn1FymGSs61zEPzFn8iE9XHJei4S/tBd7eF/E6uAUua+3HSZma3k1RMlv8SNZHWbhFkPFrqCw5WvzE+vXVbEFk/23FeTv69TVft8jWiea6kgT0zAOu7KphQ77A4YD9pM8NEWOMgagwJJGcTZR7M+YyTWgoKQkhPd2rf/eJs9/uuUDbTBXP/qLgoO0H8JZe4LZuGs5q6U6ggERhigT0awbURYhcG4a2As5sqTXaIMekSEDfzCC6exU0b5va6H80joUhSBMlzIDADDE0DTBabNjDDXugIYSA8PkghAqhKNGBhtsDuNyQXK7wVJyyfglZsZZBUWrtzSKsb1a2wMO6bgtAhDmNoD0AiRGiGNuKmPuJEaIAcHxxjDzMqHan9tuR95WzHSSENAFfSENZUO/eUujTUKYKBEN6czyXDHgUCR6XghTz1xPbfuKe0gl7AKVZxy1pAkLTECophpaSCkmEANWoJ5oGSVP129YUnhHhlBFIGW0GjYEy7IFL+KTH7JYpIPSgpJywJTpwQXg9+3MXp1URm7RU37qjinHi6nQkKGHmrx7chkCDD0ISOYZ+OSoOG11WDtrCjgNGq47CGuyy4pIE8jwCeV6Blh4NeV79dksj9IjV/by5W9R6gFAd1nSuiGgmbFzXhMDx6Rpu7KDhzYgQIcclcEkrP7p4QzhcCkQfo138+ySE70qkD3T4fsm+in4SDOefFbP58BktBAa38GFrqYL8oIRsl4bu6Zrxp06KO3CxOTixZvw9NJc7P2JsQQGcH3WxjlVU8FwI2/NgPieXtPJj7u6UGM+j/igXt/Sj0K8iIKnOHSH6pv3PrGwscTbNlqKCCL3rgzk+gwTFWFc2xvWx1o/Yt/4REO7S2CMXuLSzio2FGo4EgBYeoHeOgEtyh1872/E31C/xCoCTmwORr3JCXV9q6ZCEkBGQJXgUucLnbUQbYFjrxn/SR1RTGnuQkywYgjQiIhQC4IsINMJdUQD9C1G5LTS83riBBmQZvuISuJs1g6y4aj3QqIrIE/1y163lstQkVRPwhfQxPMqCGgp8KkqDelNUVdMP2Zumd2vJcEkxZ8eo7p9a8wt3KLUYWkZG+U+zGYBEttIxlkv2gMUeuABGiBKxXCsncBECkqbFeBwr8XIeQGSQZd0p2b7zC+jpX/iQJGu18ImBdYIR1QUrVtiC6HUBCNv6mpCwucyF/JCCHJeGHmkhyNZPxvbCxHjyy10WeSAVLCvnxdUEUBICSjUJJSpQHJLw4m8e5/aOHQvM2enB5uJQ1PgK9pNN2baFOUYDEPGrqW1bwNk3tqJ9OU56IvYnBPDq7vKPYdavHuPEs+b+cjRza0ZrjnCrDj3oEGjmFpUOLAY0U3EbAkZrlvDGzd0i4dYs9q5B1gm4cdscV6U0qEFVjM8U2y/1mj3T1O+FI2KwnwxL9l/X9RdRMU6CB7XQMKh5GX4uVVAUkpHjFjg+U4NLBiTJZZzohk+e7a+99Ti2ZeHL8vtAm+9qydbSThLCUV/MGmC/7WxdCAijvUOXtBjPd4yf5h2zQthum39hzP9Fvh6wvVbmGlY/d1u4ZH859GXCClzMwaI124CFZ6cBGW18mH8gC0fUcH/pFi4N1+YVYmBaKfw+H9KNSShlEX4vQgJkIcKBhPH8wbgtG8+pvh4cr4dV3+2zWagIv0kBx98mYV9mbx1pXjEWDYAA3MbBHdXXdz5v5naRr40U/XpFtNSM+/2ngu3iLosl1o8l9gpsVfLo647yOSt89L4jr8e4z3zfCSEgAn6IgNv2mRdr//r/ZAB9syOPo+b+lhI1Zo6WhbH+JkR+n4246viem9A2sfdjtZEstwy266EgUItjUdY3hiCNgSRB8nghfGUQaiihQMNqoaHYWmjI5QcaQghIQoKcmtaggo+mRggBf0jvzlIW0lDk11DkV/XBSzX9C6VbkeBVJOSkKAlP4VanJAmQlOg234aKvvZVKrCJDFysZbafSG3XpXitfqyTmRj3Gy1hogIURysXzfh5OyLcMUMa8yde230SjBMCIbC20IMF+zNwJBQ+6WjuUjEurwinZPpsZUH09fKeOetzUFgXQQGUaBJKVAklmoxSVUaJFv5XGnGpr6ugVJNRJlIrGQRI8GnAyoONudmz5DgvS1SapCHPHUIrVxB5rhBaukLIc4fQ0hVErqLCo8Q4AVEBlEn6vzgnNuWdxPT3CJzUWcJPxW4cVWVkKhqO9QYgQyD/SES1MRMLY6G+W2EbEE2yugbo1wUkCHh9PqRLIau/viyMPvxKeOA2Gfr/HCfEMH+xl8LhlASja154PTM76ZoBWBVYFYA1IL3t/RnjBDl8cmyurg9zG8U8fusy8jmNOKmzFksQVhcAybGqeTIo2W879hmjGNZLaqxT0d/0clsUmrdjLIu1W9u2km27MQAu7qbi+3yBw0G9FUWfZoAiZ0KIDBQVlyAzM1PvFuk4Rsn5WPa6at0X40Q93sl43HKGt7dfONaNWq+CL/blLoteFLkw5jwCiYQcCZZDmJ9v1pQoEWmXiGhtKRBuHWuEXdHrm9cjPlscPyYI22Lb56LfB+FSjM+DyM/T8KLwZyui1zPrR6zjt95j4e0l8/0eGSA5fnyIvB0nGLK3HLW2Kf/SPnGrQJznzX7bfun4PmLeFX5+w98rYFs/xm2zTOX+3ZPC33cSFectEi+sEwC0khKovpLw82I+VzECv5hvBcncU9TCmPuI+36KWR9i7DfGLhN7nIj9xQw1Yz1kvHJEljXGdceOwxdRNxx1PvJ6nM+SyM8n2zZSrMeJ8/klpWdA8jSN6XBjYQjSCEhuD9xdu+nXEww0qOEIqAJlQQ1lIQ0lAQ2Ffg2+oAa/qv+RVGTAq0jI8MrOkybSGYGLJglsORRCvk9DToqM7rneGm0RU9Xtyt+p/qVm3d5SzN6cH3X3kZCCmb/nYGK7LAxoHf6g0TQNpUGB0pCGkqBAaVCgJKChNGS7DOq/2JcGjeshYV0P1crBJB9FAvJSJP1fqn69ZYqEvBSgZYqEDBcAeCJOCozrmqYHK2ZABhHuIwLzZ3nNWlffXkASmj4yvSoQUgVCqgpV00eqh6Z3uVAUge5pAbgkvXWYJClGVwJANma1MVthQJL10EK2BRWSvkAB9BHpJbPrhx4mlJS6kZGRHj6hhfME2n7SET7hgO2+iHABVmHCT67jRNp2ci1LkMzub9Z9iC6HfdvIX8wdtyMCC/v6MS+d+3Z8+Y+xb+ex2x7HcbP8k/7worr/+39qjGVCCCgFBXBnZ/N7RhMi7CfxkSfnjhN5PVApKyiAJysL5kxaMf/OxQtYbI8hBCIeT4T3ERWqCGc5jfWF44cGLbwPRwtRc0w5ANDC+7H/gKJpjv2Gl9laXRkhTNTfDeumcV22/52SHfPbm3/DrAG+I7v9yrbr1fq7Z/sbZN+fXdygLs5y6wcVAaWoCO7MTEc4VJl9JFSOuN9XBKL/lsZYLaG/r7HWSXBfCfw9j/1nMkadqUQYHDPoiBGIRAVUkUWOuZ9Yn13R9wsAvsLC6HI2EQxBGgk5Na2+i0AJCBndWnxBDaVmt5aQHnhomoAs6S08UtwSslLkmCfxFG3dnjK8viEfR8rCzc+bpyq4uncOBrRLrceShQlhzNSj6i19zMuykIpXviv/Q+SFbwuxOMOFMiPkKGuEKUaKS5+y0Cy5FvF915pSLuJ7d0M60tM7pOL4linIS3chL01Bs1Sl3PdoVVqPmIQQ+pSnqkBIEwgaU/UC+lcRlyxZMztluGWkefSQ1C0BHpcEj6SPC2Qp58tVZU5khRDwFRTAwxNgoialMt2JIQQkjxeSN8UKSZsK59hygLMFh/0TyQxNI2/bg4mmSwgBSXFD4WdB8kq0pVsjxRCEqIo0YZ7k6oOXFvlVFAU0+EP69LSAgEeR4XVJSHfLTW5qqbqybk8ZZq85HLX8SJmK2WsOY+LAFgkHIaoWDifMqYUjQwu/qkXcjrWeFrEf/bKqHxchDdhVGKp4xRogAUhzS0hzy0j3yPqlW7aWuUQIzTJSkOaRke6WkOKS8OyaIyjwxz/lb56q4Ok/tK5yqBc5m4dAdHgiyrvPvG27DiGskELVBB5edbDCY/hr/+Y1GkyaQUdIEwiqetBhjs1gDzpSXDKau/XXwuOS4DGWexUJLrYOIyKqMZUKg4ioyWIIQpQgf0j/hd4X1FAc0FDoV+EzTn4B/VdZr0tGlleBmycuNUITAq99f7TcdeZ+cwQ/7k9FQAMCFYQYoer8dN+AeBQJaW7JCC9kI7CQY4cbnvB66R4ZKXEG1gX0AKG4uBgZEQPjjuvTLGYQZbq6d061wgPHL40VdS2ooto6hnhBhwYBGRKDDiIiIqIGhiEIUQwhTQ87ykICJUEVBT4NvpAGX0jvm69IgNeln4jmpFQ8hRxFC6gCBT4V+T4VR30q8stU5Ps0/bpx+3BpCL4KJrzwhQT+s6O0bgpdi7q38KB1ptsRbuhhhhTRYkOu85BtQLtUTBzYosF3SSpPdY7BHnSEVH0AY9Xo1yMZQYeLQQcRERFRo8AQhJKeOVtLaYxuLUFNn+3Ao0jwuiRkpDXOcTw0IbDlkL/CQUVrQlAVKPCrOFpmhBk+Dfllajjc8Kk46tMHiW3IJIRfd691KUfcluB1yeWu51aA2WuOoLCCrhhTzsxr0HVrQLtUnNw2pc7qUW0o7xjKCzoACW4j6PC4ZDSzhVH6a6yP3cEWYEREREQNH0MQSjpBNTyOR0lAP0k3u0sATa9bS00NKhrSRDjEKNMHfQ234Ai34iiuh3Ajwy1bs+skElp4Y6zniVhPn3WjZl7/a2q5O0ldkSUJPfMa7pzx5tgi1kzFxjJzwgB9uUC7TDfaZOpjhRwsMcdiYdBBRERElAwYglCTphqzteihh37iXhLUx41QNQHFmK0ltZzZWuqyFUVNS2RQ0b5tUsLdUso07C/wo1TotwuMVhv5ZXrrmNrmloFmqQpyUsx/MlbvLC13tpTqDspZF5pCd5KaogkRnrUQzpBCX27cD3MAfxG+DjPo0McQcUygJwGSsUC2ZjOVIFu39SkPFUmCJEtW2JluBB0e2z8GHURERERNF0MQalJ8IQ2+oB56FPs1FPhVfeYOTUACrF91m6UocCUwW0tDn5pVE/pgjIEY/3whDa+sL39Q0WfXHK6TKUrdMvRQI1VBM1vA4Qw8FKS5o1tfdM9NaRKtKBp7dxJN6APLqpo+pasmwi0r7CGFOdugcdUKKQT0rmVRIQX0gfplyRlSKDLgkiTIMuA2LhUZUCTZWC8cckjGPmUr6Ii8z/44TX9qQyIiIiKKjyEINVqOwUsDKgr8emsPf0g/KXPJgFeRkOGV4ZYrf+JT1alZzbEFwoGE5gwojFAmEIoOLvyqHmokemnOTFNV1Q1AXEa40cwIOKxwwww2jNAjVriRqKbUiqIhdicRQkAVergRVIGigIZAmQrNaKEB6C0v9ADCGARU0rsNKRKgGLdlWa8PZkghm+FDjJAi+j6GFERERERUNxiCUKMghL1bi0ChT0VRUEMgpCGkGYNYGmM5ZHqrP3hpSNOw4LvyW1G8sPYwFme6IgIPPZyoi9YVtUmRwt1SmqUoyI5otWGGHunVCDcqo7G3oqgvmhBQNUCNaMWhGmNmmBytLmSgWaqMVJcMr0s2pniFfikbIUgVQkUiIiIiooaAIQg1SAFVoCyo6d1aAhoKfSp8RssJIQCPovfnz06wW0s8/pCG34tD+L0ohN+Lgtb1vUVBhCoYAiMkgF2FofJXagTOPCYN3XO9yE5RkONV4FbL0KpZJuRqPK+1oSG2oqhPkd1TVA0ICQFNC7fwMbuGuGQJiiwh1a0HGykuwKPIVqjhls11gJKiILKzUxhyEBEREVGTxBCE6p2qCZSF9K4tpUE98CgJafCFBDQRHrw0zS0hJ0Wu9MmZEAJHy1RHwLGvOIS9RSFH94r6Zg7I6I0YpLHCfy4JHlnvnmCfycItA898VfHUrNf3a2a1qBACKC728wS4Htm7p4QiWnFotvE2ZCPccBmtONK8ElIUGSluWV+mSLbWG+EgpKLHJiIiIiJqyhiCUJ0SQh/Pwhy8tNCvocgcvFQVkCVj8FKXhBapcoUnbXYBVWBfcdBo1RHC78b1fcUh+MqZXaQ6uud6kJvmSiis8CrOkMK6dFVtzJJENJWpWZsCM9zQRIxWHHG6pyiyBLcsI8MjIdWlT91qhhruiJCDwRURERERUcUYglCtCqnGOB4hgdKAinyf3sUlENLHzVBsg5d6EpiWUgiBAr+md10pChmtO4LYWxTC4VK1WmNxSADy0hW0yXCjdYaC1b+VojRY/tSsU87Ia9AhQlMaVLQhMAcLVW2zo5jLIq9bXVIQnhlFlvXBQWWjZUaKS2+5Ea97itmag4iIiIiIagZDEKoxmjl4qTFjS4FPRZFfxaaDPhz1acj2yuiZ50WqW0ZWBYOXBlWBAyV6l5XwWB168FFWzVYdKS4JbTNdaJPpRpsMF9pkutAmw42WGS5HENONU7M2OfECC80YS0MzpntVHT2I9NlR9Cld9TE2zJlNZEmCIuktfdyyZLTO0AMNWda7rCiSPtaGvq4+m4p9nA4iIiIiIqo7DEGoyvxGCw9fUEOxX0NBIDx4KQBsOuDDos2FyPeFzyjtLRCEECgKaI7uK3uLQthXFMKBklC1W3W0SFOMkMOtBx2ZLrTNcCM7wXFFmlIriqY0qGi4FUZ0oKEa14XRvURA/2e+2vapWmU5HGa4ZRlulz6OhjmeiiLLcEmALOvrmLOn2MMM2QhF2BWFiIiIiKhxYAhCCQlpethRFhIoCaoo8GkoC2rwG7O1WN1aPPr4Fl/v9eGV7/Kj9nOkTMXsNYfROsOFooCGkkAFU7BUwKPorTpaZ7jQNtON1pkutM1woVWGC16XXK19A2xFUd+CxrTD/pCGoDHriSIbXUuMFhaS0RrD6wqPkeGWAbfRGkMxQgszwIgXbBARERERUdPHEISiaEIgENLH8igNChT6VBQHNQRCGkKa/qu6x6UP7JkZo1uLL6Rh3vqj5T7GvuLKTS3bPFUxuq3YWnZkuNAsVan1E9im1IqiIQuq+qC5AVvg4ZYBj0tGTqoLWV4ZqS4ZHpdkCzaMS7bGICIiIiKiBDAEIatbS1lQxb78ILTSMvhDQEDTW3l4ZMDrkpGdosQcpDGgCvxy2I9Nh/zYfNCPn48EoFWhL4tbBlrbx+nIdKNthgutM11IqYFWHdRwJBR4uGV94FAXBwclIiIiIqKawRAkydi7tZSa3VpCGnwhAU0T8JVpaKYAaW4JOUrssTMCqsAvR/TAY9MhP7YdCSBUhV4tx+d50adNCtpm6C07WqTVfquOSJoQUDVAjXMZSUBAht4Nw22bvaO2prhtCsoLPJqlupDlVZDilhh4EBERERFRrWMI0oQJc7aWkIayoECRX0VRQIPf3q1F0cdSyEiTIUFCMRRkeBXYz+cDqsC2I35sOujHZiP0CFZvKA8AwOgembXSzUQIAdWYxtS61ICQbSBNyZjxwxzYUjFm6nDLEtLdElIUvduFW9GXmw1RgioQUDWUBPVZcAKqHiqFNGEN5KoY05u6je3cipQ0Y04w8CAiIiIiooaMIUgTElDN6Wk1FAc0FPr0wMNvzNbiMrq1ZHkVuJXok08hwvv59WjA6t7yyxF/wqFH+ywXurfwYM2eMhQH4veJaZ6qoHuuN+FjM4MNzQg1QkbAoWnh5eYsIBL0IEKfmlQfPyLVI8HrkuF16QNmKpIeULhkyZiqNHy9MsyBO4OqQEDTr/sCGkqM5700KBD0a9ZzKxtTo5ozkDTmaVIDxrHHDTxSFKS4GHgQEREREVHDwRCkkVI1YU1PWxrUUOhTUWJ2axFCny1DkZDqliqcEjaoCmw7GsCmg378uK8UOwryEw492mW50DPXix55XvTI9SLLqwAAjm9ZhtlrDsfd7ureOZAlCZoQCGnhVhuaJhAS5jSoAhIkRwsLxZgRxJwNxOuS4ZUBt0t2hBouyRlw1FZXFbcxpWosIU1EhSTm66W30BEIahpUo1WOZAYkcjgoaSjBAQMPIiIiIiJqChiCNBI+o0tLWUhDoV9DkV/Vpw1V9dYFbqNbS4tUucKWBUFVYLsRemw65McvhxNv6dEu04UeeV70zPOiRwsvslKUmOsNaJeKiQNb4LXvj+KoL7zzHK+MC3tkoWO2G/uLQ/oUpsYYG4qshxpZimTMPiPHba2hNIJpTc2yprqj79OELRyxTQNbEtSDkoAqUBrUuy0J6FPC1tU4JEFVoDigIag6Aw8vAw8iIiIiImrkGII0AkFV4KcDfhT6VQgY3VoUCZleBZ44rRAitzdDj82H/Pi5EqFH20wXeuR6cXyeF91zvciOE3rYmd1yOma7MfmMPOwpCsEf0tAyzYVerVKQ4pajQg2XMX5GQw82aoosSUaQEH2fENFdbAIhgdKQQGlAQ1CLPQ6J23ge9RYkiT2XUS08hEAgqKFZqmDgQURERERETQ5DkEZAEwJ+VUN2ipzQVLEhTWDbkQA2H9IHM/3lSAABNbE5a1unyzi+Zare0iPB0EMTwmqlEtKEPrioR0HbTAUZXhmDj0ksrCGdZHX1ib5PGN2H4o5DEtJbcYSM6Y0F9IFfzRYkAKK6tHhdMpqnupCZosCrAIHSIFo2T4Vb4bTERERERETUtDAEaeBUTeDb38vwzd4y5KYp6NUqJeoX/pCmt/Qwp6z9+XDioUebzPCYHt1beOEKlSEjIwMVNSIwW3v4Q3prhFS3jLw0F3JSFWS4ZaR55KRp1VGXJEmCW0GF45D4bV1tykIaygIafEadMAOPWC08hBAoCMps8UFERERERE0SQ5AG7NPtxXjqi4M4UKJay5qnKriyVzaapypW95atlQk9MowxPYzgI8fW0kMIoLg49naaMd1uaVDvjuExWnu0ztRnm8nwsLVHQ1DeOCSqpteRxjobDRERERERUXUxBGmgPt1ejLs/2he1/EiZiufWHkl4P60z9JYeZveWnNSKu7eYolt7SGzt0Ygx/CAiIiIiomTHEKQBUjWBp744WKVtzdDDnLK2WSVCD3NsD39pCCGhj+2R5mZrDyIiIiIiImoaGII0QN/tK3N0gSlPq4jQo3klQg9Ab+3hC2rwhfSBNFUh0DbNhWapCjI8bO1BRERERERETQdDkAboUGliAcjYk3IwomtGpfZtH9vDnMklzS2jVaYLmR4ZalkQec29kBh8EBERERERURPDEKQByk1LrDVH+6zEXj5Haw/oY3vkprnQLMXZ2kMIgYIAww8iIiIiIiJqmhiCNEB9WqeiZbpSbpeY5qkKuud6Y95ntvYoM2Zycbb20IMPr0uureITERERERERNUgMQRogRZYw6bS8mLPDmK7uneMYqyNozOTiCwkICUh1SWie6kJzju1BREREREREBIAhSIM1rEsGpo1ojae+OOhoEdI8VcHVvXNwctsUlAY1tvYgIiIiIiIiShBDkAZsWJcMDOmUjrV7SvHN3jI0S1HQpZkbflXgYKnqaO2R7pGRztYeRERERERERHExBGngFFlCvzapUGQJgZAGr0tGm0wFmV629iAiIiIiIiKqDIYgjYBHkdClmQdeRe/yoshs7UFERERERERUWQxBGgFJ0qe0JSIiIiIiIqKqY18KIiIiIiIiIkoKDEGIiIiIiIiIKCkwBCEiIiIiIiKipMAQhIiIiIiIiIiSAkMQIiIiIiIiIkoKDEGIiIiIiIiIKCkwBCEiIiIiIiKipMAQhIiIiIiIiIiSQo2HIGVlZdi4cWPC6x8+fBjjx49H//79MXDgQDzyyCMIhUIx150/fz6GDRuGfv36YdSoUfjggw9qqthERERERERE1MQlHIIMGjQIP/30k2PZnDlzcOjQIceyrVu3YsyYMQkX4LbbbkNaWhpWr16Nt99+G19++SXmzZsXtd6qVavwz3/+Ey+++CK+/fZbTJgwAbfddht2796d8GMRERERERERUfJKOAQ5evSoo4WGqqp45plnsH///io/+M6dO7F27VrcddddSE1NRYcOHTB+/Hi8/vrrUetu374dQgjrn6IocLvdcLlcVX58IiIiIiIiIkoe1UoQhBDVevCff/4ZOTk5aNWqlbWsa9eu2Lt3LwoLC5GVlWUtP//88/Huu+9i5MiRUBQFkiRh+vTpaN26dZXKXd2yN0X2kImSE+tAcuPrT6wDxDpArAPEOkCNtQ4kWt56bUZRUlKC1NRUxzLzdmlpqSMECQaD6NGjBx555BH06NEDy5Ytw7333ouuXbuie/fulXrcwsJCyDLHhI0khEBpaSkAQJKkei4N1QfWgeTG159YB4h1gFgHiHWAGmsd0DQtofXqNQRJS0tDWVmZY5l5Oz093bH8oYceQr9+/dC7d28AwCWXXIL33nsPixYtwuTJkyv1uFlZWVAUpRolb5rM5Cw7O7tRVXaqOawDyY2vP7EOEOsAsQ4Q6wA11jqgqmpC69VrCHLcccchPz8fhw4dQm5uLgBg27ZtaN26NTIzMx3r7t27FyeeeKJjmcvlgtvtrvTjSpLUqF7MumQ+N3x+khfrQHLj60+sA8Q6QKwDxDpAjbEOJFrWavcJqc6T0qlTJ5x88sl49NFHUVxcjF27duH555+PObvMsGHD8Nprr2Hjxo3QNA0rV67EmjVrMHLkyOoUn4iIiIiIiIiSRKVagtx8883weDyOZTfddJOjNUYgEKhUAWbNmoUHH3wQw4cPhyzLuOiiizB+/HgAQN++fTF16lRceOGFmDBhAhRFwcSJE1FQUIBjjjkGzz33HHr27FmpxyMiIiIiIiKi5JRwCHLxxRfXSgFyc3Mxa9asmPetX7/euu5yuTBx4kRMnDixVspBRERERERERE1bwiHIY489VpvlICIiIiIiIiKqVTU2T+yRI0dqaldERERERERERDWuUiHIrl278NBDD+GTTz6xln300UcYPHgwTj/9dJxxxhlYvnx5jReSiIiIiIiIiKi6Eu4Os2vXLowZMwaBQADHH388AH0629tvvx3NmzfH5MmTsX37dtx5551o2bIl+vfvX2uFJiIiIiIiIiKqrIRDkBdeeAEtWrTA/PnzkZeXBwCYP38+VFXFU089hQEDBgDQZ4eZO3cuQxAiIiIiIiIialAS7g7z5Zdf4s9//rMVgADAqlWr0LJlSysAAYBzzjkH33//fc2WkoiIiIiIiIiomhIOQQ4dOoSOHTtat3ft2oX9+/dj4MCBjvUyMzNRUlJScyUkIiIiIiIiIqoBCYcg6enpKCgosG6vXbsWkiTh1FNPday3a9cu5OTk1FgBiYiIiIiIiIhqQsIhSJ8+ffDee+9Zt5csWQJFUTBkyBBrmRAC/+///T/07t27ZktJRERERERERFRNCQ+M+te//hXXXHMNrrrqKgghsH79elx++eVo0aIFAH3MkPnz5+O7777DK6+8UmsFJiIiIiIiIiKqioRbgpx88smYO3cuPB4PioqK8Je//AX33Xefdf+dd96JNWvW4IEHHojqIkNEREREREREVN8SbgkCAIMGDcKgQYNi3vfCCy+gU6dOyMrKqpGCERERERERERHVpEqFIOXhOCBERERERERE1JAlHIJMmTIl4Z1KkoRHH320SgUiIiIiIiIiIqoNCYcgixYtgiRJaNWqFWS5/KFEJEmqdsGIiIiIiIiIiGpSwiHIeeedh88++wx+vx/nnXcezj//fJx88sm1WTYiIiIiIiIiohqTcAgyY8YM+Hw+fPrpp1i+fDmuu+46tGjRAueffz7OP/989OzZszbLSURERERERERULZUaGDUlJQUjR47EyJEjUVxcjI8++gjLly/HvHnz0L59e1xwwQUYOXIkunTpUlvlJSIiIiIiIiKqkirPDpORkYGLL74YF198MfLz8/HRRx9hxYoVmDNnDrp164Z33323JstJRERERERERFQt5Y9wmqDS0lIUFxejtLQUqqpiz549NbFbIiIiIiIiIqIaU+WWIPv27cPKlSuxYsUKbNiwARkZGRg+fDj+7//+D6effnpNlpGIiIiIiIiIqNoqFYLs378fK1aswMqVK/Hdd98hLS0NQ4cOxQ033IAzzjgDHo+ntspJRERERERERFQtCYcgV155Jb7//nt4vV4MGTIEs2bNwpAhQ+D1emuzfERERERERERENSLhEGT9+vVQFAXHHnssjhw5gtdeew2vvfZa3PUXLFhQIwUkIiIiIiIiIqoJCYcgAwYMsK4LIcpdV5KkqpeIiIiIiIiIiKgWJByCvPrqqwmtp6oqXn/99SoXiIiIiIiIiIioNlRqYNTPP/8c77zzDiRJwujRozFkyBDH/evWrcNDDz2En3/+GePGjavRghIRERERERERVUfCIcjy5ctxxx13wOPxwO12Y8WKFZg1axZGjBiBo0eP4pFHHsH7778PRVFw3XXX1WaZiYiIiIiIiIgqLeEQZN68eTjppJPw0ksvwePx4L777sNzzz2Hrl274vrrr8e+fftwxhln4J577kHnzp1rs8xERERERERERJWWcAiyfft2PPjgg8jIyAAATJgwAeeeey4mTJiAUCiE2bNnY8SIEbVWUCIiIiIiIiKi6kg4BCkpKUGbNm2s261bt4YQAi6XC0uXLkXz5s1rpYBERERERERERDVBTnRFIQQURbFum9dvvfVWBiBERERERERE1OAlHILE07p165ooBxERERERERFRrap2CCJJUk2Ug4iIiIiIiIioViU8JggAPPDAA9bAqEIIAMDf//53pKenO9aTJAnz58+voSISEREREREREVVfwiHIgAEDAITDj3jLYt0mIiIiIiIiIqpvCYcgr776am2Wg4iIiIiIiIioVlV7TBAiIiIiIiIiosaAIQgRERERERERJQWGIERERERERESUFBiCEBEREREREVFSYAhCREREREREREmBIQgRERERERERJQWGIERERERERESUFBiCEBEREREREVFSYAhCREREREREREmBIQgRERERERERJQWGIERERERERESUFBiCEBEREREREVFSYAhCREREREREREmBIQgRERERERERJQWGIERERERERESUFBiCEBEREREREVFSYAhCREREREREREmBIQgRERERERERJQWGIERERERERESUFBiCEBEREREREVFSqPcQ5PDhwxg/fjz69++PgQMH4pFHHkEoFIq57tq1a3HppZeib9++GDJkCP75z3/WcWmJiIiIiIiIqLGq9xDktttuQ1paGlavXo23334bX375JebNmxe13rZt23DDDTfgqquuwrfffot//vOfePnll7Fy5cq6LzQRERERERERNTr1GoLs3LkTa9euxV133YXU1FR06NAB48ePx+uvvx617r///W8MHz4cF198MSRJQo8ePfDGG2/g5JNProeSExEREREREVFjU68hyM8//4ycnBy0atXKWta1a1fs3bsXhYWFjnU3bNiA9u3b44477sDAgQNx3nnnYe3atcjLy6vrYhMRERERERFRI+SqzwcvKSlBamqqY5l5u7S0FFlZWdbygoICLFiwADNmzMATTzyB9evX48Ybb0R2djb+8Ic/VOpxhRAQQlT/AJoY83nhc5O8WAeSG19/Yh0g1gFiHSDWAWqsdSDR8tZrCJKWloaysjLHMvN2enq6Y7nH48Hw4cNx1llnAQAGDBiA0aNHY8WKFZUOQQoLCyHL9T4cSoMjhEBpaSkAQJKkei4N1QfWgeTG159YB4h1gFgHiHWAGmsd0DQtofXqNQQ57rjjkJ+fj0OHDiE3NxeAPgBq69atkZmZ6Vi3a9euCAQCjmWqqlYpncrKyoKiKFUveBNlPpfZ2dmNqrJTzWEdSG58/Yl1gFgHiHWAWAeosdYBVVUTWq9eQ5BOnTrh5JNPxqOPPooHH3wQR48exfPPP48xY8ZErXvFFVfgL3/5C5YsWYILL7wQX3/9NZYtW4Ynn3yy0o8rSVKjejHrkvnc8PlJXqwDyY2vP7EOEOsAsQ4Q6wA1xjqQaFnrvU/IrFmzEAqFMHz4cFx22WU444wzMH78eABA3759sXTpUgDAoEGD8Pzzz2PBggU4+eSTMWXKFNx9990YPnx4fRafiIiIiIiIiBqJem0JAgC5ubmYNWtWzPvWr1/vuD1kyBAMGTKkLopFRERERERERE1MvbcEISIiIiIiIiKqCwxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCgxBiIiIiIiIiCgpMAQhIiIiIiIioqTAEISIiIiIiIiIkgJDECIiIiIiIiJKCvUeghw+fBjjx49H//79MXDgQDzyyCMIhULlbrN161acdNJJWLNmTR2VkoiIiIiIiIgau3oPQW677TakpaVh9erVePvtt/Hll19i3rx5cdcvKyvDpEmT4PP56q6QRERERERERNTo1WsIsnPnTqxduxZ33XUXUlNT0aFDB4wfPx6vv/563G2mTp2Ks88+uw5LSURERERERERNQb2GID///DNycnLQqlUra1nXrl2xd+9eFBYWRq2/ePFi7Ny5ExMmTKjLYhIRERERERFRE+CqzwcvKSlBamqqY5l5u7S0FFlZWdbybdu2YcaMGVi4cCEURanW4wohIISo1j6aIvN54XOTvFgHkhtff2IdINYBYh0g1gFqrHUg0fLWawiSlpaGsrIyxzLzdnp6urXM7/fj9ttvxz333IO2bdtW+3ELCwshy/U+HEqDI4RAaWkpAECSpHouDdUH1oHkxtefWAeIdYBYB4h1gBprHdA0LaH16jUEOe6445Cfn49Dhw4hNzcXgN7io3Xr1sjMzLTW++GHH7Bjxw7ce++9uPfee63lN910E0aPHo0HHnigUo+blZVV7dYkTZGZnGVnZzeqyk41h3UgufH1J9YBYh0g1gFiHaDGWgdUVU1ovXoNQTp16oSTTz4Zjz76KB588EEcPXoUzz//PMaMGeNYr3///tiwYYNjWffu3TFnzhwMHDiw0o8rSVKjejHrkvnc8PlJXqwDyY2vP7EOEOsAsQ4Q6wA1xjqQaFnrvU/IrFmzEAqFMHz4cFx22WU444wzMH78eABA3759sXTp0nouIRERERERERE1BfXaEgQAcnNzMWvWrJj3rV+/Pu52W7b8//buPaqqMv/j+Ac8EOAdccnKsSy5GKJCHmEmmLS84AVtUkyXSKIzWoOJ5IRiLpsms8kp7w5mipc1kve8Dmu0potagOKtybKEMp2hTCGwRFQ4+/dHy/PrBJJaecLn/VrrrMV59n7Ofs55vnL5uJ+9P/q5hgQAAAAAAG5Cbj8TBAAAAAAA4EYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEYgBAEAAAAAAEZwewhSUlKilJQU2e12RUdHa8aMGaqqqqp139WrVysuLk6RkZGKi4tTdnb2DR4tAAAAAACor9wegqSlpcnPz0+7d+/Whg0blJubqxUrVtTY7/XXX9fs2bM1c+ZMHThwQM8//7zmzp2rHTt23PhBAwAAAACAesetIchnn32mvXv3Kj09Xb6+vmrTpo1SUlJqPcPj1KlTGjNmjCIiIuTh4aHIyEhFR0dr3759bhg5AAAAAACob2zuPPixY8fUrFkztWrVytnWrl07FRcX6+zZs2rSpImzPTEx0aVvSUmJ9u3bpylTplzzcS3LkmVZ1z/wm9Tlz4XPxlzUgNmYf1ADoAZADYAaQH2tgasdr1tDkHPnzsnX19el7fLziooKlxDku06fPq1HHnlE4eHhio+Pv+bjnj17Vp6ebl8J9ItjWZYqKiokSR4eHm4eDdyBGjAb8w9qANQAqAFQA6ivNeBwOK5qP7eGIH5+fjp//rxL2+XnDRs2rLXPoUOHNGHCBNntdv31r3+VzXbtb6F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",
+      "text/plain": [
+       "<Figure size 1100x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(11, 6))\n",
+    "\n",
+    "# Plot mean ± std band\n",
+    "ax.plot(train_sizes, train_rmse_mean, 'o-', color='#3498db', linewidth=2.5, markersize=6, label='Training error')\n",
+    "ax.fill_between(train_sizes, train_rmse_mean - train_rmse_std, train_rmse_mean + train_rmse_std, alpha=0.2, color='#3498db')\n",
+    "\n",
+    "ax.plot(train_sizes, val_rmse_mean, 's-', color='#e74c3c', linewidth=2.5, markersize=6, label='Validation error (5-fold CV)')\n",
+    "ax.fill_between(train_sizes, val_rmse_mean - val_rmse_std, val_rmse_mean + val_rmse_std, alpha=0.2, color='#e74c3c')\n",
+    "\n",
+    "ax.set_xlabel('Training Set Size', fontsize=12)\n",
+    "ax.set_ylabel('RMSE', fontsize=12)\n",
+    "ax.set_title('Learning Curve: Bias-Variance Tradeoff', fontsize=14, fontweight='bold')\n",
+    "ax.legend(fontsize=11)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.set_ylim([0, 1.5])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65e70916",
+   "metadata": {},
+   "source": [
+    "**Reading this plot:**\n",
+    "- **Left side (small training size):** Both errors are high → **high bias** (model too simple, underfitting)\n",
+    "- **Middle:** Validation error rises → **high variance** (overfitting on small dataset)\n",
+    "- **Right side (full data):** Both errors converge → model stabilises\n",
+    "\n",
+    "To reduce overfitting, you can:\n",
+    "1. Collect more data (right side of the curve — diminishing returns)\n",
+    "2. Simplify the model (lower degree polynomial)\n",
+    "3. **Regularization** — add a penalty for complex models (next section)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "318a0cd9",
+   "metadata": {},
+   "source": [
+    "## Part 6: Regularization — Ridge, Lasso, ElasticNet\n",
+    "\n",
+    "### 6.1 The idea behind regularization\n",
+    "\n",
+    "Instead of minimising just the error:\n",
+    "$$J(\\theta) = \\frac{1}{m} \\|X\\theta - y\\|^2$$\n",
+    "\n",
+    "We add a **penalty term** that grows with the size of the coefficients:\n",
+    "\n",
+    "**Ridge (L2):** $J(\\theta) = \\text{MSE} + \\alpha \\sum \\theta_j^2$\n",
+    "\n",
+    "**Lasso (L1):** $J(\\theta) = \\text{MSE} + \\alpha \\sum |\\theta_j|$\n",
+    "\n",
+    "**ElasticNet:** $J(\\theta) = \\text{MSE} + \\alpha(\\text{L1 ratio} \\cdot \\text{L1 penalty} + (1 - \\text{L1 ratio}) \\cdot \\text{L2 penalty})$\n",
+    "\n",
+    "The **α parameter** (regularization strength) controls the tradeoff:\n",
+    "- **α = 0:** No penalty, just normal linear regression (can overfit)\n",
+    "- **α large:** Heavy penalty, coefficients shrink toward zero (underfits)\n",
+    "\n",
+    "The key difference:\n",
+    "- **Ridge** shrinks all coefficients smoothly\n",
+    "- **Lasso** can shrink some coefficients all the way to zero (automatic feature selection!)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6def43f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions at x=2.0:\n",
+      "  Linear Regression: 7.6005\n",
+      "  Ridge (α=0.1):     7.5993\n",
+      "  Lasso (α=0.1):     6.2468\n",
+      "  ElasticNet (α=0.1): 6.5214\n",
+      "\n",
+      "Coefficients (x and x²):\n",
+      "  Linear Reg:  [[0.91317014 0.68162567]]\n",
+      "  Ridge:       [0.91282656 0.68152677]\n",
+      "  Lasso:       [0.51443494 0.5659413 ]\n",
+      "  ElasticNet:  [0.59532946 0.58942149]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Reza Yaghobi\\AppData\\Local\\Temp\\ipykernel_16808\\3428913648.py:23: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  poly_lr_pred = float(poly_lr.predict(X_test_point_poly)[0])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge, Lasso, ElasticNet\n",
+    "\n",
+    "# Test point\n",
+    "X_test_point = np.array([[2.0]])\n",
+    "X_test_point_poly = poly_features.transform(X_test_point)\n",
+    "\n",
+    "# Ridge regression (L2 penalty)\n",
+    "ridge = Ridge(alpha=0.1)\n",
+    "ridge.fit(X_poly, y_quad)\n",
+    "ridge_pred = ridge.predict(X_test_point_poly)[0]\n",
+    "\n",
+    "# Lasso regression (L1 penalty)\n",
+    "lasso = Lasso(alpha=0.1)\n",
+    "lasso.fit(X_poly, y_quad)\n",
+    "lasso_pred = lasso.predict(X_test_point_poly)[0]\n",
+    "\n",
+    "# ElasticNet (L1 + L2)\n",
+    "elastic = ElasticNet(alpha=0.1, l1_ratio=0.5)  # 50% L1, 50% L2\n",
+    "elastic.fit(X_poly, y_quad)\n",
+    "elastic_pred = elastic.predict(X_test_point_poly)[0]\n",
+    "\n",
+    "# Convert to float for formatting\n",
+    "poly_lr_pred = float(poly_lr.predict(X_test_point_poly)[0])\n",
+    "ridge_pred = float(ridge_pred)\n",
+    "lasso_pred = float(lasso_pred)\n",
+    "elastic_pred = float(elastic_pred)\n",
+    "\n",
+    "print(f\"Predictions at x=2.0:\")\n",
+    "print(f\"  Linear Regression: {poly_lr_pred:.4f}\")\n",
+    "print(f\"  Ridge (α=0.1):     {ridge_pred:.4f}\")\n",
+    "print(f\"  Lasso (α=0.1):     {lasso_pred:.4f}\")\n",
+    "print(f\"  ElasticNet (α=0.1): {elastic_pred:.4f}\")\n",
+    "\n",
+    "print(f\"\\nCoefficients (x and x²):\")\n",
+    "print(f\"  Linear Reg:  {poly_lr.coef_}\")\n",
+    "print(f\"  Ridge:       {ridge.coef_}\")\n",
+    "print(f\"  Lasso:       {lasso.coef_}\")\n",
+    "print(f\"  ElasticNet:  {elastic.coef_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb6b1f63",
+   "metadata": {},
+   "source": [
+    "### 6.2 Visualize regularization effect on coefficients\n",
+    "\n",
+    "As α increases, coefficients shrink. Lasso aggressively zeros out weak features.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "9d7d1ee1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "alphas = np.logspace(-3, 2, 50)\n",
+    "ridge_coefs = []\n",
+    "lasso_coefs = []\n",
+    "\n",
+    "for alpha in alphas:\n",
+    "    ridge = Ridge(alpha=alpha)\n",
+    "    ridge.fit(X_poly, y_quad)\n",
+    "    ridge_coefs.append(ridge.coef_)\n",
+    "    \n",
+    "    lasso = Lasso(alpha=alpha, max_iter=10000)\n",
+    "    lasso.fit(X_poly, y_quad)\n",
+    "    lasso_coefs.append(lasso.coef_)\n",
+    "\n",
+    "ridge_coefs = np.array(ridge_coefs)\n",
+    "lasso_coefs = np.array(lasso_coefs)\n",
+    "\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Ridge\n",
+    "ax1.plot(alphas, ridge_coefs[:, 0], 'o-', label='x coefficient', linewidth=2)\n",
+    "ax1.plot(alphas, ridge_coefs[:, 1], 's-', label='x² coefficient', linewidth=2)\n",
+    "ax1.set_xscale('log')\n",
+    "ax1.set_xlabel('Regularization strength (α)', fontsize=12)\n",
+    "ax1.set_ylabel('Coefficient value', fontsize=12)\n",
+    "ax1.set_title('Ridge Regression: Smooth coefficient shrinkage', fontsize=12, fontweight='bold')\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "ax1.legend()\n",
+    "\n",
+    "# Lasso\n",
+    "ax2.plot(alphas, lasso_coefs[:, 0], 'o-', label='x coefficient', linewidth=2, color='#e74c3c')\n",
+    "ax2.plot(alphas, lasso_coefs[:, 1], 's-', label='x² coefficient', linewidth=2, color='#e67e22')\n",
+    "ax2.set_xscale('log')\n",
+    "ax2.set_xlabel('Regularization strength (α)', fontsize=12)\n",
+    "ax2.set_ylabel('Coefficient value', fontsize=12)\n",
+    "ax2.set_title('Lasso Regression: Coefficients can shrink to zero', fontsize=12, fontweight='bold')\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "ax2.legend()\n",
+    "ax2.axhline(y=0, color='k', linestyle='--', alpha=0.5)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2795661a",
+   "metadata": {},
+   "source": [
+    "## Part 7: Logistic Regression — Classification\n",
+    "\n",
+    "### 7.1 From regression to classification\n",
+    "\n",
+    "Linear regression predicts a **continuous value**. For classification (yes/no), we need a **probability**.\n",
+    "\n",
+    "Logistic regression wraps the linear prediction inside a **sigmoid function**:\n",
+    "\n",
+    "$$P(y=1 | x) = \\sigma(\\theta^T x) = \\frac{1}{1 + e^{-\\theta^T x}}$$\n",
+    "\n",
+    "The sigmoid function always outputs a value between 0 and 1 (a probability). The decision rule is:\n",
+    "- If σ > 0.5 → predict class 1\n",
+    "- If σ ≤ 0.5 → predict class 0\n",
+    "\n",
+    "The sigmoid is **steep near 0.5** (confident predictions) and **flat at the extremes** (already confident).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "2de36326",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data shape: X=(150, 1), y=(150,)\n",
+      "Classes: [False  True]\n",
+      "Class distribution: [100  50]\n",
+      "\n",
+      "Train/test split: 112 train, 38 test\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Load Iris dataset\n",
+    "iris = load_iris(as_frame=True)\n",
+    "X_iris = iris.data[['petal length (cm)']].values\n",
+    "y_iris = iris.target_names[iris.target] == 'setosa'  # Binary: is it setosa?\n",
+    "\n",
+    "print(f\"Data shape: X={X_iris.shape}, y={y_iris.shape}\")\n",
+    "print(f\"Classes: {np.unique(y_iris)}\")\n",
+    "print(f\"Class distribution: {np.bincount(y_iris.astype(int))}\")\n",
+    "\n",
+    "# Split into train/test\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_iris, y_iris, random_state=42)\n",
+    "print(f\"\\nTrain/test split: {X_train.shape[0]} train, {X_test.shape[0]} test\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb18f6a7",
+   "metadata": {},
+   "source": [
+    "### 7.2 Fit a logistic regression model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "ba5003c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logistic regression fitted.\n",
+      "  Intercept: 7.3375\n",
+      "  Coefficient: -2.7369\n",
+      "  Classes: [False  True]\n",
+      "\n",
+      "Test accuracy: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "# Fit logistic regression\n",
+    "log_reg = LogisticRegression(random_state=42)\n",
+    "log_reg.fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"Logistic regression fitted.\")\n",
+    "print(f\"  Intercept: {log_reg.intercept_[0]:.4f}\")\n",
+    "print(f\"  Coefficient: {log_reg.coef_[0, 0]:.4f}\")\n",
+    "print(f\"  Classes: {log_reg.classes_}\")\n",
+    "\n",
+    "# Predictions on test set\n",
+    "accuracy = log_reg.score(X_test, y_test)\n",
+    "print(f\"\\nTest accuracy: {accuracy:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51d6664c",
+   "metadata": {},
+   "source": [
+    "### 7.3 Visualize the sigmoid and decision boundary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "62ce7e10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a grid of x-values\n",
+    "X_grid = np.linspace(0, 7, 1000).reshape(-1, 1)\n",
+    "\n",
+    "# Get probability of being Setosa (class 1)\n",
+    "y_proba = log_reg.predict_proba(X_grid)\n",
+    "\n",
+    "# Decision boundary: where P = 0.5\n",
+    "db_x = X_grid[np.argmin(np.abs(y_proba[:, 1] - 0.5))]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(11, 6))\n",
+    "\n",
+    "# Plot sigmoid curves\n",
+    "ax.plot(X_grid, y_proba[:, 0], '--', color='#e74c3c', linewidth=2.5, label='P(not Setosa)')\n",
+    "ax.plot(X_grid, y_proba[:, 1], '--', color='#3498db', linewidth=2.5, label='P(is Setosa)')\n",
+    "\n",
+    "# Decision boundary\n",
+    "ax.axvline(db_x, color='k', linestyle=':', linewidth=2, label=f'Decision boundary (x={db_x[0]:.2f})')\n",
+    "ax.axhline(0.5, color='gray', linestyle='--', alpha=0.5)\n",
+    "\n",
+    "# Training data\n",
+    "ax.scatter(X_train[y_train == 0], np.zeros(sum(y_train == 0)) + 0.02, \n",
+    "           marker='x', s=100, color='#e74c3c', linewidth=2, label='Non-Setosa (train)')\n",
+    "ax.scatter(X_train[y_train == 1], np.ones(sum(y_train == 1)) - 0.02, \n",
+    "           marker='+', s=100, color='#3498db', linewidth=2, label='Setosa (train)')\n",
+    "\n",
+    "ax.set_xlabel('Petal length (cm)', fontsize=12)\n",
+    "ax.set_ylabel('Probability', fontsize=12)\n",
+    "ax.set_title('Logistic Regression: Sigmoid probability and decision boundary', fontsize=13, fontweight='bold')\n",
+    "ax.legend(fontsize=10, loc='center left')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.set_ylim([-0.1, 1.1])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7dff5f9",
+   "metadata": {},
+   "source": [
+    "### 7.4 Make predictions\n",
+    "\n",
+    "The model outputs probabilities via `predict_proba()`, and binary predictions via `predict()`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "30ac38f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = 2.5: P(not setosa) = 0.379, P(setosa) = 0.621 → Predict: True\n",
+      "x = 3.0: P(not setosa) = 0.705, P(setosa) = 0.295 → Predict: False\n",
+      "x = 3.5: P(not setosa) = 0.904, P(setosa) = 0.096 → Predict: False\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test on specific points\n",
+    "test_points = np.array([[2.5], [3.0], [3.5]])\n",
+    "\n",
+    "probas = log_reg.predict_proba(test_points)\n",
+    "preds = log_reg.predict(test_points)\n",
+    "\n",
+    "for i, (x, proba, pred) in enumerate(zip(test_points, probas, preds)):\n",
+    "    print(f\"x = {x[0]:.1f}: P(not setosa) = {proba[0]:.3f}, P(setosa) = {proba[1]:.3f} → Predict: {pred}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c96432a7",
+   "metadata": {},
+   "source": [
+    "## Part 8: Multiclass Classification with Softmax\n",
+    "\n",
+    "### 8.1 From binary to multiclass\n",
+    "\n",
+    "So far we predicted: Setosa or not.\n",
+    "\n",
+    "Now let's predict the **actual class** among all three Iris species (Setosa, Versicolor, Virginica) using the two petal measurements.\n",
+    "\n",
+    "For multiclass, logistic regression uses **softmax** instead of sigmoid:\n",
+    "\n",
+    "$$P(y=k | x) = \\frac{e^{\\theta_k^T x}}{\\sum_j e^{\\theta_j^T x}}$$\n",
+    "\n",
+    "Softmax is just the generalization of sigmoid: it outputs a probability distribution over all k classes that sum to 1.\n",
+    "\n",
+    "Scikit-learn automatically handles this — just pass multiclass y to `LogisticRegression`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "380067a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Multiclass problem: predict one of 3 classes\n",
+      "Classes: ['setosa' 'versicolor' 'virginica']\n",
+      "Train size: 112, Test size: 38\n",
+      "\n",
+      "Test accuracy (multiclass): 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Multiclass problem: all 3 species\n",
+    "X_multi = iris.data[['petal length (cm)', 'petal width (cm)']].values\n",
+    "y_multi = iris.target\n",
+    "\n",
+    "X_train_m, X_test_m, y_train_m, y_test_m = train_test_split(X_multi, y_multi, random_state=42)\n",
+    "\n",
+    "print(f\"Multiclass problem: predict one of {len(np.unique(y_multi))} classes\")\n",
+    "print(f\"Classes: {iris.target_names}\")\n",
+    "print(f\"Train size: {X_train_m.shape[0]}, Test size: {X_test_m.shape[0]}\")\n",
+    "\n",
+    "# Fit multiclass logistic regression\n",
+    "log_reg_multi = LogisticRegression(random_state=42)\n",
+    "log_reg_multi.fit(X_train_m, y_train_m)\n",
+    "\n",
+    "accuracy_multi = log_reg_multi.score(X_test_m, y_test_m)\n",
+    "print(f\"\\nTest accuracy (multiclass): {accuracy_multi:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "436dd22e",
+   "metadata": {},
+   "source": [
+    "### 8.2 Visualize the multiclass decision boundaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "5e9723e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a 2D mesh for decision boundary visualization\n",
+    "petal_length_range = np.linspace(0, 7, 500)\n",
+    "petal_width_range = np.linspace(0, 3, 500)\n",
+    "petal_length_grid, petal_width_grid = np.meshgrid(petal_length_range, petal_width_range)\n",
+    "\n",
+    "# Flatten the grid for prediction\n",
+    "X_mesh = np.c_[petal_length_grid.flatten(), petal_width_grid.flatten()]\n",
+    "y_mesh_proba = log_reg_multi.predict_proba(X_mesh)\n",
+    "\n",
+    "# Get the probability of class 1 (Versicolor) for coloring\n",
+    "y_mesh_class1 = y_mesh_proba[:, 1].reshape(petal_length_grid.shape)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(11, 8))\n",
+    "\n",
+    "# Contour plot of class probabilities\n",
+    "contour = ax.contourf(petal_length_grid, petal_width_grid, y_mesh_class1, \n",
+    "                       levels=15, cmap='RdYlBu_r', alpha=0.8)\n",
+    "cbar = plt.colorbar(contour, ax=ax, label='P(Versicolor)')\n",
+    "\n",
+    "# Plot training data\n",
+    "colors = ['#e74c3c', '#3498db', '#2ecc71']\n",
+    "for i, (class_name, color) in enumerate(zip(iris.target_names, colors)):\n",
+    "    mask = y_train_m == i\n",
+    "    ax.scatter(X_train_m[mask, 0], X_train_m[mask, 1], \n",
+    "              c=color, s=100, alpha=0.7, edgecolors='black', linewidth=1,\n",
+    "              label=class_name, marker='o')\n",
+    "\n",
+    "ax.set_xlabel('Petal length (cm)', fontsize=12)\n",
+    "ax.set_ylabel('Petal width (cm)', fontsize=12)\n",
+    "ax.set_title('Logistic Regression (Softmax): Multiclass Decision Boundaries', fontsize=13, fontweight='bold')\n",
+    "ax.legend(fontsize=11, loc='upper left')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e74a840",
+   "metadata": {},
+   "source": [
+    "### 8.3 Decision boundaries form regions\n",
+    "\n",
+    "Each point in the 2D space is assigned to the class with the **highest probability**. The contours show where probabilities transition — these are the decision boundaries.\n",
+    "\n",
+    "Different sklearn models (SVM, neural networks, etc.) produce different boundary shapes, but the logic is the same: partition the feature space into regions, one per class.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "9b7443d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test point: petal length=3.5, petal width=1.5\n",
+      "\n",
+      "Softmax probabilities:\n",
+      "  P(setosa      ) = 0.0821\n",
+      "  P(versicolor  ) = 0.8956\n",
+      "  P(virginica   ) = 0.0223\n",
+      "\n",
+      "Predicted class: versicolor\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions and show softmax outputs\n",
+    "test_point = np.array([[3.5, 1.5]])\n",
+    "proba_multi = log_reg_multi.predict_proba(test_point)\n",
+    "pred_multi = log_reg_multi.predict(test_point)\n",
+    "\n",
+    "print(f\"Test point: petal length={test_point[0,0]}, petal width={test_point[0,1]}\")\n",
+    "print(f\"\\nSoftmax probabilities:\")\n",
+    "for i, (class_name, prob) in enumerate(zip(iris.target_names, proba_multi[0])):\n",
+    "    print(f\"  P({class_name:12s}) = {prob:.4f}\")\n",
+    "print(f\"\\nPredicted class: {iris.target_names[pred_multi[0]]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e361ee5c",
+   "metadata": {},
+   "source": [
+    "## Summary: The Linear Model Zoo\n",
+    "\n",
+    "| Model | Task | Link function | Cost | When to use |\n",
+    "|-------|------|---|---|---|\n",
+    "| Linear Regression | Continuous prediction | Identity | MSE | Default for regression |\n",
+    "| Ridge Regression | Regression (with penalty) | Identity | MSE + L2 | When features are correlated |\n",
+    "| Lasso Regression | Regression + feature selection | Identity | MSE + L1 | When you suspect many features are irrelevant |\n",
+    "| ElasticNet | Regression (balanced penalty) | Identity | MSE + L1 + L2 | Best of both Ridge and Lasso |\n",
+    "| Logistic (binary) | Binary classification | Sigmoid | Log loss | Interpretable classifier |\n",
+    "| Logistic (multiclass) | Multiclass classification | Softmax | Cross-entropy | Multiple classes, interpretable |\n",
+    "\n",
+    "All these models **learn** coefficients (θ) using the same optimization principles:\n",
+    "- **Normal equation** for direct solution (small datasets)\n",
+    "- **Gradient descent** for iterative solution (large datasets)\n",
+    "- **Regularization** to prevent overfitting\n",
+    "\n",
+    "The only changes are the link function (sigmoid vs. identity) and the cost function (MSE vs. log loss).\n"
+   ]
+  }
+ ],
+ "metadata": {
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