Real Estate Price Prediction Using Machine Learning Models

Accurate real estate price prediction is crucial in today’s market to aid buyers, sellers, and investors in making informed decisions. This study employs machine learning algorithms—specifically Linear Regression, Decision Tree Regression, and Random Forest Regression—to model and predict housing prices based on various influential features. The methodology involves data preprocessing, feature engineering, and model evaluation using standard metrics like R² score and Root Mean Squared Error (RMSE). The models are trained on real-world housing datasets, and results demonstrate the efficiency of ensemble learning over traditional linear approaches. This paper establishes that Random Forest offers the most accurate predictions and is suitable for practical applications in real estate.