Value Prediction of Real Estate Using Machine Learning

Real estate is property in the form of land and houses. Its prices depend on location, number of bedrooms, society, area, etc. India is second in the world regarding population growth, so affordable housing becomes very necessary. The real estate business is very vast. So, customers or sellers need to know the estimated price of the houses. This research covers that area and predicts housing prices. This work uses k-fold cross-validation and linear regression model. In K-fold cross-validation, the data is divided into k different subsets which are also called fold. A linear regression model is a machine learning model that describes the relations between a dependent variable and one or more independent variables. The data used in this work is for Bangalore state. The results obtained from this linear regression model are compared to real data. The results from this study prove that this model can accurately predict the prices of buildings and houses. Furthermore, this accurate prediction will help customers and sellers, and directly impact the economy.