The objective of this project is to predicting the final sale price of a house The data is collected from Kaggle. The data set consists of 1460 observations with 81 variables. All the predictors explain the various features of the house, the data frame consists of one output variable 'Sale Price' Data cleaning, Data Visualization were performed. ML algorithms such as Linear Regression, Ridge Regression, Lasso Regression are used to explore the positive and negative coefficients that influence the final Sale Price.