This Case Study is based on identification of the risky loan applicants, which helps in cutting down the amount of credit loss by performing Exploratory Data Analysis. There are different categorical and continuous variables describing the behaviour of loan applicants, their personal information, the amount of loan they took, the interest they are paying, etc. The objective is to analyse the data provided and find those attributes which are essential for identifying the risky applicants.
- The lending club case study is a project to analyse the pattern of behaviour of loan applicants.
- The aim is to identify patterns which indicate if a person is likely to default, which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc.
- The loan dataset containing information on the customers of a lending club is used here.
- python v3.10
- numpy v1.20.3
- pandas v1.3.4
- matplotlib v3.4.3
- seaborn v0.11.2
- This project is a part of UpGrad Course. I would like to thank the team at UpGrad and IIITB for providing us the chance to work on this project.