HOUSE PRICE PREDICTION

The rapid urbanization and escalating property prices in metropolitan cities have made housing affordability a critical concern, especially for middle-class families. Accurate prediction of house prices is essential for buyers, sellers, and real estate agents to make informed decisions and minimize financial risks. This paper presents a machine learning-based system for predicting house prices using a Linear Regression model trained on a comprehensive dataset of residential properties. The system incorporates data preprocessing, feature extraction, and model evaluation to ensure reliable and interpretable predictions. A web-based interface built with Flask allows users to input property details and receive instant price estimates. Experimental results demonstrate the model’s effectiveness in capturing key factors influencing house prices, providing a valuable tool for the real estate market. Future enhancements include integrating advanced algorithms and real-time market data to improve prediction accuracy and usability.