This project has used different features of the house that determine the price of the house. This project tries to build a regression model using regularisation in order to predict the actual value for house sale price prediction. Lasso and Ridge techniques weere compared to find the best model. The ridge model was chosen because there are many features that determine the price of the house. The RMSE was lower for ridge model as compare to the lasso model. Root Mean_squared_error for train set with ridge model is 0.54 Root Mean_squared_error for test set with ridge model is 0.57 R2 score for train set with ridge model 0.70 R2 score for test set with ridge model 0.68
The project has been deployed on aws server the project can be viewed at this link: http://ec2-3-21-28-165.us-east-2.compute.amazonaws.com/