Machine Learning based Comparative Study on House Price Prediction Task

Because it enables buyers and sellers to make informed decisions about purchasing and selling real estate, house price prediction is important. Additionally, it aids banks and other financial organizations in determining the worth of a property when taking into account a mortgage or loan application. It also aids real estate agents and brokers in pricing homes appropriately. The use of machine learning and neural networks to anticipate home prices has been extensively studied. In one publication, an approach is put out for predicting housing prices with real inputs utilizing a variety of regression techniques. In order to examine the precision of the four models—linear regression, random forest regressor, XGBoost regressor, and support vector machine regressor—in predicting Boston home prices, no study has been done. The importance of this study is to evaluate how well these four models forecast Boston home values.