The model is expected to help the management understand the key factors affecting the prices of house.
- A US-based Housing company wants to understand the variables affecting the sale of houses.
- The company wants to enter into Australian Housing market.
- Trying to understand the contributing factors which can give us insights in to the Australian housing market when the company starts to operate in Australian market.
- Data set used is the Housing market data from US which has the features which may or may not be affect the prices of the houses.(train.csv)
- Ridge and Lasso Regression Model are built with optimum alpha calculated in GridSearchCV method. Optimum alpha = 9.0 for ridge and 0.0001 for lasso model.
- Model evaluation is done with R2 score and Root Mean Square Error.
- Lasso Regression is chosen as final model for having slightly better R-square value on test data.
- Out of 50 features in the final model, top 10 features in order of descending importance are ['1stFlrSF', '2ndFlrSF', 'OverallQual', 'OverallCond', 'SaleCondition_Partial', 'LotArea', 'BsmtFinSF1','SaleCondition_Normal', 'MSZoning_RL', 'Neighborhood_Somerst']
- library - Pandas
- library - NumPy
- library - matplotlib
- library - Seaborn
- library - sklearn
- library - statsmodel
Give credit here.
- This project was completed as part of Advanced Regression Assignment...
- This project was based on Ridge Regression and Lasso Regression.
Created by [@HiteshKongadi] - feel free to contact me!