/LendingClubCaseStudy

Primary LanguageJupyter Notebook

Lending Club Casestudy

  • A consumer finance company which specialises in lending various types of loans to urban customers wants to understand the driving factors (or driver variables) behind loan default. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are 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

Table of Contents

General Information

In this project we are determining if a loan will be defaulted or not. We are trying to find the factors leading to loan default. In this case study, we will use EDA to understand how consumer attributes and loan attributes influence the tendency of default. There can be 2 possible Scenarios here.

  • 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

Here we are analysing the factors that can lead the applicant to not likely pay the loan and vice versa.

The datset contains the complete loan data for all loans issued through the time period 2007 t0 2011.

Conclusions

  • Interest rate more than 13% have higher chance of Charge Off
  • Those who are not ownig a home have high chances of Charged Off
  • Loan for small business have high changes of Charged Off
  • High revol_util rate have more chances of Charged Off
  • Higher the number of bankrupties, higher the chance of Charged Off
  • Loan Grades F and G have high chances of Charged Off
  • Lower the Grade, higher the chance of Charged Off
  • 5 year loans have more chances of Charged Off
  • State NE have the high chance of Charged Off

Technologies Used

  • IDE - VSCode
  • library - pandas==1.1.5
  • library - numpy==1.21.6
  • library - matplotlib==3.5.3
  • library - seaborn==0.12.2
  • library - plotly==5.15.0

Contact

Created by [@VKVishnuVinod],[@Vedant950] feel free to contact us!