Lending Club Case Study

Lending club is the largest online loan marketplace, facilitating personal loans, business loans, and financing of medical procedures. Borrowers can easily access lower interest rate loans through a fast online interface. . Like most other lending companies, lending loans to ‘risky’ applicants is the largest source of financial loss (called credit loss). The credit loss is the amount of money lost by the lender when the borrower refuses to pay or runs away with the money owed. In other words, borrowers who default cause the largest amount of loss to the lenders. In this case, the customers labelled as 'charged-off' are the 'defaulters'.

If one is able to identify these risky loan applicants, then such loans can be reduced thereby cutting down the amount of credit loss. Identification of such applicants using EDA is the aim of this case study.

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

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

• Lending club wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. It can utilise this knowledge for its portfolio and risk assessment.
• We are using Loan data given by Lending Club.

Conclusions

• We found Negatively correlated features with Loan status as Defaulters. They are home_ownership, annual_inc and verification_status.
• We found Postively correlated features with Loan status as Defaulters. They are term, int_rate, grade.
• In detail conclusion can be found in the pdf.

Technologies Used

• python 3.9
• pandas - version 1.3.4
• numpy - version 1.20.3
• matplotlib - version 3.4.3
• seaborn - version 0.11.2

Acknowledgements

Give credit here.

• This project was inspired by EDA module as part of Data Science course.
• This project was based on https://learn.upgrad.com/course/1991/segment/16593/125701/384598/2002849.

Contact

Created by @rahul2july, @kc11381 - feel free to contact us!

Contributors -Rahul Gupta -Kanhaiya Choudhary