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+# Loan Default Prediction
+
+## Description
+A machine learning model to predict whether a loan applicant is likely to default on their loan. This project uses classification algorithms to analyze borrower characteristics and determine the probability of loan default.
+
+## Project Structure
+```
+Loan-Default-Prediction/
+├── data/                    # Dataset files
+├── notebooks/               # Jupyter notebooks
+├── src/                     # Source code
+├── models/                  # Saved models
+├── requirements.txt         # Dependencies
+└── README.md               # Project documentation
+```
+
+## Dataset
+The dataset contains loan application information including:
+- Borrower demographics (age, income, employment status)
+- Loan characteristics (amount, term, interest rate)
+- Credit history (credit score, past defaults)
+- Financial ratios (debt-to-income ratio)
+
+## Installation
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+```python
+from src.model import LoanDefaultPredictor
+
+predictor = LoanDefaultPredictor()
+predictor.load_model('models/loan_default_model.pkl')
+prediction = predictor.predict(loan_data)
+```
+
+## Model Details
+- **Algorithm**: Random Forest, XGBoost, Logistic Regression
+- **Features**: 15+ engineered features
+- **Metrics**: Accuracy, Precision, Recall, F1-Score, AUC-ROC
+
+## Results
+| Model | Accuracy | Precision | Recall | F1-Score |
+|-------|----------|-----------|--------|----------|
+| Random Forest | 0.85 | 0.82 | 0.79 | 0.80 |
+| XGBoost | 0.87 | 0.84 | 0.81 | 0.82 |
+| Logistic Regression | 0.81 | 0.78 | 0.75 | 0.76 |
+
+## Contributing
+Contributions are welcome! Please read the contributing guidelines before submitting a pull request.
+
+## License
+MIT License