We are provided with real world data of the largest online marketplace facilitating personal loans, business loans, and financing of medical procedures. The goal is to identify the risky applicants based on the EDA techniques and share the recommendations for the same.
Loan dataset given of applicants to which loan has been issued. We have to apply the EDA techniques learnt so far to make business sense out of it and provide insights to reduce credit loss in business.
Make verification status stringent. Check for public record bankruptcies and derogatory public records High debt to income ratio risky. Check credit card loans in WY, MT, UT, TN Missing employment records risky. Any delinquency is high risk. High number of inquiries are high
Pandas - version 1.5.3 NumPy - version 1.24.3 Seaborn - version 0.12.2 MatplotLib - version 3.7.1
- This project was based on the lending club case study given by UpGrad as a part of the AI and ML course from IIIT Bangalore.
Created by [AashnaBehl17] - feel free to contact me!