Lending Club Case Study

The Goal is to predict the SalesPrice of houses in Australia for a US-Based companies so that they make decisions to buy and sell houses profitably . Also identify the top predictors so that the company can get an idea on the general trend.

Table of Contents

• General Info
• Technologies Used
• Conclusions

General Information

• The assigment is about predicting house prices
• The model used to predict the target variable is regression model , the metric used to measure goodness of fit is r-squared
• Regularization techniques like Ridge and Lasso have been applied to reduce overfitting and make the model more generic and robust
• MinMax Scaler was used for scaling numerical data
• Backward Feature Selection was used

Technologies Used

• pandas - version 1.5.3
• numpy - version 1.24.2
• matplotlib - version 3.7.1
• seaborn - version 0.12.2
• re - version 2.2.1
• Scikit-learn 1.1
• SciPy 1.11.3
• statsmodel 0.14.0

Conclusions

• The 5 most important fetures to the model are '1stFlrSF', 'OverallQual', '2ndFlrSF', 'LotArea', 'OverallCond'
• The Ridge Scores are 1 . R2 Score of Ridge Training - 0.91 2 . R2 Score of Ridge Testing - 0.9
• The Lasso Scores are 1. R2 Score of Lasso Training - 0.9049 2. R2 Score of Lasso Testing - 0.9