Initial commit: housing regression, MNIST classification, linear models

projects/notebooks/housing-price-regression/README.md

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+# 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)