Lending club case study using EDA

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

LendingClub is USA’s largest lending marketplace, connecting borrowers with investors since 2007.

Both borrower and investor are registered as a member of Lending club. Borrower applied for loan, then lending club platform perform some risk analysis on the applied loan and pass it to investors with approved ammount ( lenders/Investors ).

Lenders ( Investors ) makes money from the interest they get from money they lend and Lending club makes money from source fees and other fees.

Business Problem : Business suffers loss if borrower get defaulter.

Goal : The company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment.

Data Set : loan.csv

• Borrowers with middle income group ( 40K to 60K ) with loan amount above 15K.
• Borrowers accross all income groups with interest rate more than 12%.
• Borrowers with middle income group ( 40K to 60K ) has taken higher loan amount with home ownership as mortgage.
• Loans for longer duration.
• Loan amount 11K to 14K with purpose as small business, cerit card, wedding, debt consolidation and house.
• Loan with loan grade C with sub grade C1 and C2, grade D with sub grade D2 and D3, grade E with sub grade E1 and E1 having loan amount betweein 10K to 15K.

• Python - 3.9.12
• numpy - 1.21.5
• pandas - 1.4.2
• matplotlib
• seaborn - 0.11.2

Give credit here.

• This project was inspired by upGrade AI & ML course case study

Created by @sandipanp - feel free to contact me!