Real Estate Cost Prediction Through Web Application Using Random Forest Machine Learning Algorithm and Python Flask

Machine learning has become progressively sophisticated and sturdy due to the development of new algorithms. A compelling application is observed in general is the prediction of the house price. The model which can be created using ML can help the customers to get an accurate price of the house. These Price Prediction models make use of attributes of the house under consideration namely location, count of bedrooms, tax rates, Number of years the house has existed, and commercial complexes in a nearby location. Price prediction forecasting for houses is a critical characteristic of real estate. The objective of this project is to uncover Crucial patterns and trends from historical property records. Historical property data from Bengaluru is subjected to machine learning scrutiny to extract valuable insights. The diverse factors influencing house prices have stimulated the curiosity of researchers across various domains. The research utilizes numerous regression models to investigate the determinants of house price changes. This research leverages various feature selection techniques, and feature scaling techniques to augment the efficacy of the prognostic model. The effectiveness of machine learning models is evaluated based on various metrics, including precision, correctness, and susceptibility.