Initial commit: housing regression, MNIST classification, linear models

projects/notebooks/classification-project/README.md

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+# 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)