TAMPABOREL/loan-risk-cameroon

Loan Default & Credit Risk Analysis - Cameroon Banking Sector # Loan Default & Credit Risk Analysis ## Cameroon Banking Sector ### Project Overview This project analyzes loan default patterns across 1,000 borrower records from the Cameroonian banking sector. Using Python and SQL, it identifies key risk factors that contribute to loan defaults. ### Tools Used - Python (Pandas, NumPy, Matplotlib) - SQL (SQLite) - Jupyter Notebook ### Dataset - 1,000 synthetic Cameroonian loan records - 11 features including region, income, credit score, loan purpose - Target variable: defaulted (0 = No, 1 = Yes) ### Key Findings - Overall default rate: 10.8% - Est region has the highest default rate - Immobilier loans carry the highest default risk - Poor credit score borrowers default at 6x higher rate than Excellent credit score borrowers ### Project Structure ``` loan-risk-project/ ├── data/ │ ├── loan_data_cameroon.csv │ └── loan_database.db ├── notebooks/ │ └── loan_risk_analysis.ipynb ├── queries/ │ └── loan_risk_queries.sql ├── charts/ │ ├── default_by_region.png │ ├── default_by_purpose.png │ ├── credit_score_distribution.png │ └── loan_vs_income.png └── README.md ``` ### How to Run 1. Clone this repository 2. Install requirements: pip install pandas numpy matplotlib jupyter 3. Open Jupyter Notebook 4. Run notebooks/loan_risk_analysis.ipynb ### Author TAMPA BOREL - aspiring Data Analyst