The data set consists of features of houses and neighborhood in Boston. Exploratory data analysis is used to understand the relatioships between the input variables and target feature. Linear Regression is used to understand the statistical significance of features. The data is split into train and test sets and cross validation is implemented to obtain the best score. Visualizations like regression plot and residual plots are plotted using seaborn library to understand the predictions made by the ML algorithm.