This project analyzes a dataset of 2 million+ loan records to assess credit risk, identify bad loan patterns, and visualize lending trends. The goal was to build a robust data pipeline using Python, SQL, and Power BI to help stakeholders minimize default rates.
- Python: Pandas (Data Cleaning, Regex extraction, Outlier removal).
- MySQL: Advanced Analysis (CTEs, Window Functions, Case Statements).
- Power BI: Interactive Dashboard (DAX Measures, Risk Visualization).
- Risk vs. Income: High-income borrowers have a significantly lower default rate than low-income borrowers.
- Term Length: 60-month loans are riskier than 36-month loans, showing a default rate of over 12%.
- Risk Grades: There is a clear correlation between lower credit grades (D-G) and higher "Charged Off" rates.
The raw dataset used for this analysis is available on Kaggle:
Note: Due to GitHub's file size limits, the raw CSV file and the full Power BI (.pbix) file are not hosted in this repository. Please refer to the Dashboard Snapshot above to view the interactive results.