This project focuses on exploratory data analysis (EDA) and financial insights using Lending Club loan data from Kaggle. I performed data cleaning, visualization, and trend analysis to uncover key financial patterns, loan defaults, and risk factors.
✅ Data Source: Kaggle (Lending Club Loan Data)
✅ Visualization Tool: Tableau
✅ Key Analysis Areas: Loan status, interest rates, borrower risk profiling, and lending trends
✅ Interactive Dashboard: Published on Tableau Public
🎯 View my Tableau Dashboard: Northeastern RAF Lender Data
📂 Kaggle Dataset: Lending Club Insightful Financial EDA
- Extracted Lending Club loan dataset from Kaggle.
- Removed missing values, duplicates, and irrelevant columns.
- Standardized data formats (e.g.,
loan_status,interest_rate).
- Loan Default Analysis: Identified patterns in loan defaults based on credit grades.
- Interest Rate Distribution: Analyzed interest rate variations by loan amount and term.
- Borrower Segmentation: Grouped borrowers based on income, loan purpose, and credit history.
- Risk Profiling: Assessed risk factors affecting loan approval and repayment success.
✅ Loan Status Distribution (Charged Off, Fully Paid, Current)
✅ Interest Rate Trends by loan amount and term
✅ Top Reasons for Loan Defaults
✅ Credit Score Impact on Loan Approval
✅ Debt-to-Income Ratio Analysis
📌 Higher credit scores correlated with lower interest rates and higher approval rates.
📌 Short-term loans had significantly higher interest rates compared to long-term loans.
📌 Debt-to-income ratio was a critical factor in determining borrower risk.
📌 Top reasons for loan defaults included debt consolidation and small business loans.
🔹 Feature Engineering: Add more derived features for better insights.
🔹 Predictive Modeling: Implement ML models to forecast loan defaults.
🔹 More Interactive Visuals: Enhance Tableau dashboards with drill-down analytics.
🔹 Real-time Data Updates: Automate dataset updates for ongoing analysis.