Using EDA, to determine whether the applicant will be deafaulted or not, in order to prevent financial loss and gain insights from the Lending Club Loan data. Finding the driving factors that indicate a loan is likely to default.
- To identify patterns using EDA that indicate if the loan applicant is likely to default or not and identify the variables that are strong indicators of loan default.
- Business Problem: Lending loans to ‘risky’ applicants is the largest source of financial loss. In other words, borrowers who default cause the largest amount of loss to the lenders.To identify these risky loan applicants to reduce the financial loss using EDA is the aim of this case study.
- Loan amount and interest rate were high for the borrower who has charged off. So, the loan amount and interest rate should be determined based on their annual income, loan grade and other aspects.
- Loan applicants without home ownership, unverified loan source and with high loan amounts are more likely to default.
- Borrowers with loan grades A and B are less likely to default.
- The loan borrowers count for Debt Consolidation is high. So, if the dti value is high applicant is likely to default.
- Applicants with 0 derogatory public records have a less chance of loan default.
- Python - version 3.10.12
- NumPy - version 1.23.5
- Pandas - version 1.5.3
- Matplotlib - version 3.7.1
- Seaborn - version 0.12.2