This project focuses on analyzing bank loan data to evaluate lending performance, borrower risk, and repayment behavior. The analysis transforms raw loan data into meaningful insights using Python, helping financial institutions understand loan quality and portfolio health.
The project includes KPI calculations, Good Loan vs Bad Loan analysis, and multiple visualizations to identify trends, risks, and opportunities.
- Analyze total loan applications and monthly trends
- Track total funded amount and total amount received
- Evaluate borrower risk using interest rate and DTI metrics
- Compare Good Loans vs Bad Loans
- Identify patterns based on region, loan purpose, employment length, and home ownership
- Total Loan Applications
- Month-to-Date (MTD) Loan Applications
- Total Funded Amount
- MTD Total Funded Amount
- Total Amount Received
- MTD Total Amount Received
- Average Interest Rate
- Average Debt-to-Income Ratio (DTI)
- Good Loan Application Percentage
- Good Loan Applications
- Good Loan Funded Amount
- Good Loan Total Received Amount
- Bad Loan Application Percentage
- Bad Loan Applications
- Bad Loan Funded Amount
- Bad Loan Total Received Amount
The project includes the following visualizations:
-
Monthly Loan Trends (Line / Area Chart)
- Identifies seasonality and long-term lending trends
-
Regional Analysis by State (Bar Chart)
- Highlights regions with high or low lending activity
-
Loan Term Analysis (Donut Chart)
- Shows distribution of loans across different term lengths
-
Employment Length Analysis (Bar Chart)
- Examines how employment history affects loan applications
-
Loan Purpose Breakdown (Bar Chart)
- Analyzes reasons borrowers seek loans
-
Home Ownership Analysis (Tree Map / Heat Map)
- Evaluates impact of home ownership on loan disbursement and applications
Metrics visualized include:
- Total Loan Applications
- Total Funded Amount
- Total Amount Received
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
├── Bank_Loan_Analysis.ipynb ├── data/ │ └── bank_loan_data.csv ├── README.md
- Clear separation between Good and Bad loan performance
- Identification of high-risk borrower segments
- Understanding of regional and demographic lending trends
- Actionable insights for improving credit decision-making
This project demonstrates how Python can be used to perform end-to-end financial data analysis. It highlights strong analytical thinking, KPI-driven analysis, and visualization skills relevant for Data Analyst and Data Scientist roles in the banking and finance domain.
Chaitra Huralikuppi