Property Price Prediction using Advanced Machine Learning Models
This project aims to accurately predict real estate prices by experimenting with multiple powerful regression models and robust evaluation techniques.
Project Highlights:
Data Preparation: Loaded and cleaned a real estate dataset, removing non-informative features such as titles, dates, and postal codes. Missing values were handled by dropping incomplete entries.
? Train/Test Splitting: Data was split into 80% training and 20% testing sets for fair model evaluation.
? Model Training: Trained and compared the following ML models:
LightGBM Regressor
XGBoost Regressor
CatBoost Regressor ?
Random Forest Regressor
Stacking Regressor
Performance Evaluation: Models were assessed using multiple metrics:
R² Score for prediction accuracy
MAE (Mean Absolute Error)
MSE (Mean Squared Error)
Visualization & Interpretability: Used heatmaps, scatter plots, and bar charts to explore relationships. SHAP values provided model explainability, and Partial Dependence Plots offered deeper insights into feature impact.
Model Optimization: Applied cross-validation techniques to validate performance consistency and enhance model robustness.
Objective:
To deliver a reliable and interpretable property price forecasting tool that can support smarter real estate decisions.
