Table of Contents

• Table of Contents
• Lending Club Case Study
• Dataset
• Conclusions
• Technologies Used
• Contributors

Lending Club Case Study

EDA to understand how consumer attributes and loan attributes influence the tendency of default.

Lending Club a renowned finance company which lends loans to customers has to go through few steps before approving a loan. When the applicant drafts his or her willingness for loan, the company must check the eligibility of each customer before processing a loan. There are two types of risks associated with the bank's decision

• If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company
• If the applicant is not likely to repay the loan, i.e., he/she is likely to default then approving the loan may lead to a financial loss for the company.

Dataset

Conclusions

• Defaulter rate increase with increase in interest rate
• Borrowers opting loans for small business, have tendency to default
• Majority of verified borrowers who are opting for high loan amounts in turn for high interest rates also tending to default

Technologies Used

• Kernel:
  • Python 3.8
• Computing libraries:
  • Pandas
  • Numpy
• Plotting libraries:
  • Matplotlib
  • Seaborn

Contributors

• Vinay Narayana
• Maka Sitaram