Machine Learning Based House Price Forecasting

House price prediction is one of the most important applications of machine learning in the realestate sector. With increasing urbanization and rapid growth of housing markets, accurate prediction of property prices has become crucial for buyers, sellers, investors, and financial institutions. This research focuses on developing a machine learning model using Linear Regression, Decision Tree, and Random Forest algorithms to estimate housing prices based on features such as location, area, number of bedrooms, bathrooms, and other structural parameters. The system integrates a web-based front-end with a Python Flask backend and utilizes a trained machine learning model serialized using a pickle file. The proposed model aims to improve decision-making, reduce valuation errors, and automate price estimation. Results show that the system achieves more than 95% prediction accuracy, demonstrating the effectiveness of the model. This paper also discusses the applications, challenges, and future scope of the system.