• Prof. Pallavi Chandratre
• Shweta Bangar
• Shrushti Ramdas
• Shraddha Kothe

Predicting house prices and loan prices is of paramount importance in the real estate and financial industries. This project aims to develop a web application using the flask framework integrated with Machine Learning models to predict house prices, loan prices, and rent prices. The application will provide users with a user-friendly interface where they can input relevant property and financial information. The ML models, trained on historical data, will then generate accurate predictions for house prices, loan prices, and rent prices. Users can make informed decisions about buying, financing, or renting properties, contributing to a more efficient real estate market. It aims to enhance the efficiency and transparency of the real estate and financial industries. By providing users with accurate and data-driven insights, it contributes to a more informed market, ultimately benefiting not only individual property buyers and investors but also the broader real estate ecosystem. The system will be designed with scalability, user-friendliness, and data security in mind, ensuring it aligns with the highest standards of the industry.

House Price Prediction, Home Loan Prediction, Home Rent Prediction

"HOUSE PRICE VISION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.131-136, July-2024, Available :http://www.jetir.org/papers/JETIRGK06017.pdf

"HOUSE PRICE VISION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. pp131-136, July-2024, Available at : http://www.jetir.org/papers/JETIRGK06017.pdf