Boston House Price Prediction Using Regression Models

Everyone wishes to buy and live in a house which suits their lifestyle and which provides amenities according to their needs. There are many factors that are to be taken into consideration like area, location, view etc. for prediction of house price. It is very difficult to predict house price as it is constantly changing and quite often the prices are exaggerated for which people who want to buy houses, and various real estate agencies who want to invest in properties, find it difficult to buy or sell houses. For this reason, in this paper the author creates an advanced automated Machine Learning model using Simple Linear Regression, Polynomial Regression, Ridge Regression and Lasso Regression using the Boston house dataset to predict house price in future accurately, and to measure the accuracy of these models various measuring metrics like R-Squared, Root Mean Square Error (RMSE) and Cross-Validation are used. This paper also studies the correlation of various attributes of the Boston dataset using the heat map to see which attributes actually impact the prediction of the models. It removes the outliers which are present in the dataset to achieve good accuracy. In this paper it is observed that Lasso Regression performs better in all the measuring matrices whereas Simple Linear Regression performs poor in all of the measuring matrices.