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

EDA — explored feature distributions, geographic price spread, and correlation with the target. median_income was the strongest predictor (r ≈ 0.69).
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.
Feature engineering — derived ratio features (rooms_per_house, etc.) that are more semantically meaningful per-household than the raw per-block counts.
Preprocessing pipeline — all transforms wrapped in sklearn Pipeline/ColumnTransformer to guarantee no data leakage and identical transforms on train/test.
Model comparison — Linear Regression vs Decision Tree baselines, evaluated with 5-fold cross-validation (not just training accuracy).
Hyperparameter tuning — GridSearchCV over tree depth and an RBF-kernel feature transform.
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

Notebooks

housing-ml-notebook.ipynb — original notebook, my own work
housing_ml_notebook_polished.ipynb — same analysis with AI-assisted documentation and formatting added for readability

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