DESIGN AND IMPLEMENTATION OF A MACHINE LEARNING-BASED HOUSE PRICE PREDICTION SYSTEM
Abstract
This project focuses on the design and implementation of a machine learning-based house price prediction system. Rising real estate demand necessitates accurate price forecasting to aid buyers, sellers, and investors. The study employs regression algorithms (e.g., Linear Regression, Random Forest, and XGBoost) to analyse housing datasets, including features like location, size, and amenities. The methodology involves data preprocessing, model training, and evaluation using metrics such as RMSE and R². Results indicate that XGBoost outperforms other models with 92% accuracy. The system is implemented as a web application using Python (Flask) and deployed for real-time predictions. This project contributes to automated real estate valuation, reducing human bias and improving market transparency.
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