Flask-Powered Machine Learning Model for Accurate Real Estate Price Prediction

Real estate price prediction is a critical task for various stakeholders, including buyers, sellers, and investors. Existing methods often face challenges such as limited data availability, inadequate feature engineering, and the inability to adapt to dynamic market conditions. This research proposes a novel approach to address these limitations by leveraging machine learning and a Flask-based web application. The proposed system utilizes a comprehensive dataset encompassing various property attributes, including area type, location, total square footage, number of bathrooms, and balconies. A machine learning model is trained on this data to accurately predict real estate prices. The user-friendly web interface allows users to input property details and obtain real-time price estimates. The system's effectiveness is demonstrated through rigorous evaluation, showcasing its ability to provide reliable predictions and assist users in making informed decisions. The integration of machine learning and web technology offers a valuable tool for the real estate industry.