Add docs with detailed write-ups and result visualizations

docs/Housing-Price-Regression.md

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+# Housing Price Regression
+
+End-to-end regression pipeline predicting median house value from California census-block data (20,640 rows).
+
+## The question driving the approach
+
+Which features actually predict house value, and can a simple, interpretable pipeline get close to what a more complex model would achieve?
+
+## Exploratory analysis
+
+Before modeling, checking which raw features correlate with the target:
+
+![Correlation with target](images/housing-correlation.png)
+
+`median_income` stood out as by far the strongest predictor (r ≈ 0.69) — every later decision, including the stratified train/test split, was built around that finding.
+
+## Model comparison via cross-validation
+
+Rather than trusting a single train-set score, both baseline models were evaluated with 5-fold cross-validation to get an honest estimate of real-world error:
+
+![Cross-validation MAE comparison](images/housing-cv-comparison.png)
+
+## What the tuned model actually relies on
+
+After `GridSearchCV` tuning, inspecting which engineered features the Decision Tree leaned on most:
+
+![Feature importance](images/housing-feature-importance.png)
+
+The engineered ratio features (rooms-per-house, etc.) pulled real weight here — not just the raw census counts, which justified the extra feature engineering step.
+
+## Final result
+
+Test set touched exactly once, after every modeling decision was locked in:
+
+![Actual vs predicted](images/housing-actual-vs-predicted.png)
+
+| Metric | Value |
+|---|---|
+| MAE | $40,600 |
+| RMSE | $61,901 |
+| R² | 0.72 |
+| 95% CI on RMSE | [$59,141 — $64,542] |
+
+The scatter shows the model tracks the trend well but underpredicts at the high end — expected, since expensive homes are rarer and harder to fit precisely with limited high-price training examples.
+
+## Notebooks
+
+- [`housing-ml-notebook.ipynb`](../housing-price-regression/housing-ml-notebook.ipynb) — original work
+- [`housing_ml_notebook_polished.ipynb`](../housing-price-regression/housing_ml_notebook_polished.ipynb) — AI-assisted documentation pass