Lending 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 utilize this knowledge for its portfolio and risk assessment.

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

• Indentify the indicators causing loan charge off.
• Lending company wants to understand the driving factors (or drivervariables) behind loan default
• Using the loan dataset from year 2007 to 2011

• Persons with high annual income are less likely to default.
• Small Business purpose loans are having high default percentage.
• Public derogatory records and public barnkruptcy records need to be checked before approving the loan.
• High interest rate loans are high probability to default.
• Person in less grade is less likely to default (Grade A being less)
• Loan to Income ratio need to be considered to provide loan.
• More loans are taken to repurpose old loans.

• jupyter notebook v2023.10.1100000000

Give credit here.

• This project was inspired by lending club case study upgrad
• References if any upgrad
• This project was based on this tutorial.

Created by [@nithin399] - feel free to contact me!