LendingClubCaseStudy

ByPriyavarshiniM&RamkumarP

We have plotted graphs based on the understanding of the Loans.csv provided. The work contains data understanding, cleaning, univariate and bivariate analysis.

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

• Identifying markers for default loans
• Loans.csv dataset provided by Lending club

Conclusions

Default loans are driven by the following factors:

Loan amount House Ownership Interest rate Annual Income Grade Term

Technologies Used

• Python 3.11
• Pandas
• Seaborn

Acknowledgements

Give credit here.

• This project was inspired by Upgrad
• Also, we have referred Akashdeep's (one of our Upgrad coach) colab notes (https://colab.research.google.com/drive/1fHNxUdS2PM5cGSoOf6Z-y2wlDtlLYUsg#scrollTo=wJK_9znMsFOk) and google for help with code syntax

Contact

Created by @PriyaVMN and Ramkumar- feel free to contact me!