Project Name

Lending Club Case Study

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

• Analysis of loan data for finding the factors to identify.
• We are using EDA for finding the patterns in loan data.
• As a part of a Consumer Lending Finance Company, which specialises in lending various types of loans, we need to identify the patterns which indicates if a loan is likely to Default.
• loan.csv is the dataset we used in this analysis.

Conclusions

• For higher loan amount, there is higher chance of getting charged off.
• Higher interest rates is directly related to getting charged off.
• If dti is more for a person, his loan have high chance of getting charged off.
• Loan issued to grade D, E, F, G are most likely to be charged off.
• Loans given to small business, renewable energy and education are more likely to be charged off.
• Loans given to people from Nebraska are 60% charged off. One loan in every five loans(22.5%) given to Nevada people is likely to be charged off.

Technologies Used

• python
• pandas
• numpy
• matplotlib
• seaborn

Acknowledgements

Give credit here.

• This project was inspired by Upgrad and IIIT-B.
• This project was based on Upgrad tutorial.

Contact

Created by @eswar-janjanam and @Pragyan-Choudhury