End-to-end machine learning system that predicts Bengaluru residential property prices. Covers the full data science pipeline - EDA, feature engineering, model training, evaluation, and an interactive desktop dashboard for real-time inference.
- Exploratory Data Analysis with outlier detection (3σ rule)
- Feature Engineering: price_per_sqft, location grouping, encoding
- 5-model comparison: Linear, Ridge, Lasso, Random Forest, XGBoost
- Best model: Random Forest - R² = 0.91, RMSE = ₹7.8L
- Cross-validation (k=5), MAE, RMSE, R² evaluation suite
- Interactive desktop dashboard via CustomTkinter for live prediction
- Model serialization with Joblib for production deployment
