- This task will predict using two models Ridge and Lasso Regression
- This will help business to determine which house to buy
- A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia.
- We have a training dataset added with the code which have all features for the House and based on these features are needed to make selection for which property to buy
- After running the models we came into conclusion that we can use Ridge over Lasso because it has better r^2
- These are the important predictive variables
- LotArea------------- Lot size in square feet
- OverallQual--------Rates the overall material and finish of the house
- OverallCond-------Rates the overall condition of the house
- YearBuilt-------- ---Original construction date
- BsmtFinSF1-------Type 1 finished square feet
- TotalBsmtSF------Total square feet of basement area
- GrLivArea----------Above grade (ground) living area square feet
- TotRmsAbvGrd---Total rooms above grade (does not include bathrooms)
- Street_Pave-------Pave road access to property
- RoofMatl_Metal--Roof material_Metal
- Ridge
- R2 score(Train)--------- 0.88 ----------------------------0.88
- Lasso
- R2 score(Test)-----------0.87-----------------------------0.86
- Pandas - version 1.4.3
- Numpy - version 1.23.2
- Sklearn - version 1.1.2
- Seaborn - version 0.11.2
- Matplotlib - version 3.5.3
- This project was inspired by Upgrad Tutorials
- Refrerences - Kaggle, Sklearn Documentation
Created by [@sudeepignition] - feel free to contact me!