50 lines
2.0 KiB
Markdown
50 lines
2.0 KiB
Markdown
# Housing Price Regression
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End-to-end regression pipeline predicting median house value from California census-block data (20,640 rows).
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## The question driving the approach
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Which features actually predict house value, and can a simple, interpretable pipeline get close to what a more complex model would achieve?
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## Exploratory analysis
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Before modeling, checking which raw features correlate with the target:
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`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.
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## Model comparison via cross-validation
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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:
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## What the tuned model actually relies on
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After `GridSearchCV` tuning, inspecting which engineered features the Decision Tree leaned on most:
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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.
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## Final result
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Test set touched exactly once, after every modeling decision was locked in:
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| Metric | Value |
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| MAE | $40,600 |
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| RMSE | $61,901 |
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| R² | 0.72 |
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| 95% CI on RMSE | [$59,141 — $64,542] |
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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.
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## Notebooks
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- [`housing-ml-notebook.ipynb`](../housing-price-regression/housing-ml-notebook.ipynb) — original work
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- [`housing_ml_notebook_polished.ipynb`](../housing-price-regression/housing_ml_notebook_polished.ipynb) — AI-assisted documentation pass
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