Property Price Prediction: Exploring Advanced Models for Accurate Forecasting

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.