House Price Prediction System using Machine Learning

The House Price prediction system research proposes an innovative framework to forecast residential property prices by leveraging data-driven methodologies and advanced computational techniques. The System integrates a broad range of input variables, including property attribute, along with spatial and socio-economic context derived from the neighborhood’s characteristics. The study explores the effectiveness of machine learning algorithms, including regression trees, boosted decision models, and ensemble learning approaches, to uncover underlying patterns that price variation. Key data preparation steps, such as outlier detection, feature encoding and data normalization, are employed to optimize model performance. The model’s predictive power is rigorously assessed using evaluation criteria like root mean squared deviation (RMSD) and explanatory power (R²), ensuring robust and reliable results. This methodology provides real estate investors, developers and homeowners with an intuitive tool for pricing strategy optimization and market trend forecasting.