House Price Prediction Using Machine Learning

Abstract: Real estate is the least transparent industry in our ecosystem. Housing prices keep changing day in and day out and sometimes are hyped rather than being based on valuation. Predicting housing prices with real factors is the main crux of our research project. This paper outlines how to predict housing costs using various regression techniques using the Python library. The proposed method takes into account the sophisticated aspects used in the house price calculation and provides a more accurate forecast. This paper uses machine learning to explain how the house price model works and which datasets are used in the proposed model. Predictive models to determine the selling prices of homes in cities like Bangalore are maintained because of more than difficult and sophisticated tasks. The price of real estate for sale in cities like Bangalore depends on several factors involved. Keywords: Housing Price Prediction , linear regression, XGBoost,, Machine Learning, Lasso Regression, Ridge Regression, Random Forest Regression.