AI-Powered Real Estate Price Predictor

Accurate property price estimation is a critical challenge in the real estate industry, where traditional methods often rely on subjective assessments and time-consuming manual analyses. This paper presents a web-based application developed using the Flask framework that leverages advanced machine learning algorithms—Random Forest, XGBoost, and LightGBM—for precise real estate price prediction. The proposed system enables users to input key property features such as the number of bedrooms, bathrooms, square footage, and location, and receive instant, data-driven price estimates. The application incorporates distinct functionalities for both Admin and User roles: Admins manage user accounts, FAQs, and user queries, while Users can register, log in, submit property details for prediction, and access support resources. By integrating robust machine learning models with an intuitive user interface and efficient role-based management, this solution enhances transparency, accuracy, and efficiency in property valuation. The results demonstrate the practical benefits of applying machine learning to real estate, streamlining the decision-making process for buyers, sellers, and professionals, and contributing to a more reliable and data-driven marketplace.