Overview
Built a linear regression model enriched with feature engineering and cross-validation, then combined it with a Random Forest in an ensemble to achieve 89% accuracy on unseen California housing data.
Features
- Feature Engineering: Created interaction & polynomial features
- Ensemble Approach: Blended Linear Regression + Random Forest
- Cross-Validation: Ensured robustness across folds
Built With
- Python & scikit-learn
- pandas & NumPy
- Jupyter Notebook