Loan Default Rate Prediction Model

A comprehensive machine learning project to predict the probability of loan defaults using borrower and loan characteristics.

Project Overview

Objective: Develop an accurate machine learning model to identify high-risk loans and minimize financial losses for lenders.

Dataset: Loan_default.csv containing borrower profiles and loan information with features like:

• Credit score
• Loan amount
• Interest rate
• Borrower's income
• Debt-to-income ratio (DTI)
• Employment length
• Home ownership status
• Loan purpose
• Past payment history
• Loan status (target variable)

Project Structure

Project_5/
├── Loan_default.csv              # Original dataset
├── loan_default_prediction.ipynb # Main Jupyter notebook
├── examine_data.py               # Data exploration script
├── requirements.txt              # Python dependencies
├── README.md                     # This file
├── loan_default_model.pkl        # Saved trained model (generated)
└── model_comparison_results.csv  # Model performance comparison (generated)

Features

1. Data Processing

• Missing Value Handling: Median imputation for numerical features
• Outlier Detection: IQR-based outlier removal
• Feature Engineering:
  • Income-to-loan ratio
  • Age groups categorization
  • Credit score categories
  • DTI ratio categories
• Categorical Encoding: One-hot encoding for categorical variables
• Feature Scaling: StandardScaler normalization

2. Class Imbalance Handling

• SMOTE: Synthetic Minority Oversampling Technique
• Balanced Training: Ensures equal representation of default/non-default cases

3. Machine Learning Models

• Logistic Regression: Linear baseline model
• Random Forest: Ensemble tree-based model
• XGBoost: Gradient boosting classifier

4. Model Evaluation

• Metrics: Accuracy, Precision, Recall, F1-Score, AUC-ROC
• Visualization: ROC curves, confusion matrices, performance comparisons
• Hyperparameter Tuning: GridSearchCV for optimal parameters

5. Model Interpretation

• Feature Importance: Analysis of most predictive features
• Performance Comparison: Side-by-side model evaluation

Setup Instructions

Prerequisites

• Python 3.7 or higher
• pip package manager

Installation

1. Clone or download the project files

2. Install dependencies:

   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook

4. Open the main notebook:

   • Navigate to loan_default_prediction.ipynb
   • Run all cells sequentially

Usage

Running the Complete Pipeline

1. Data Exploration:

   python examine_data.py

2. Full ML Pipeline:

   • Open loan_default_prediction.ipynb
   • Execute all cells to run the complete pipeline

Using the Trained Model

After training, the model is saved as loan_default_model.pkl. Use it for predictions:

import joblib
import pandas as pd

# Load the saved model
model_artifacts = joblib.load('loan_default_model.pkl')
model = model_artifacts['model']
scaler = model_artifacts['scaler']
feature_columns = model_artifacts['feature_columns']

# Prepare new data (ensure same features as training)
new_data = pd.DataFrame({
    # Add your loan data here with same column names
})

# Make predictions
new_data_scaled = scaler.transform(new_data[feature_columns])
predictions = model.predict(new_data_scaled)
probabilities = model.predict_proba(new_data_scaled)[:, 1]

print(f"Default Probability: {probabilities[0]:.4f}")
print(f"Prediction: {'Default' if predictions[0] == 1 else 'No Default'}")

Key Results

The notebook provides:

1. Comprehensive EDA: Data distribution analysis, correlation matrices, feature relationships
2. Model Comparison: Performance metrics for all three models
3. Best Model Selection: Automated selection based on AUC-ROC score
4. Hyperparameter Tuning: Optimized parameters for the best model
5. Feature Importance: Insights into most predictive features
6. Production-Ready Model: Saved model with preprocessing pipeline

Model Performance Metrics

The models are evaluated using:

• Accuracy: Overall correct predictions
• Precision: True positives / (True positives + False positives)
• Recall: True positives / (True positives + False negatives)
• F1-Score: Harmonic mean of precision and recall
• AUC-ROC: Area under the receiver operating characteristic curve

Business Impact

• Risk Assessment: Early identification of high-risk loans
• Loss Reduction: Minimize financial losses through better screening
• Decision Support: Data-driven loan approval process
• Regulatory Compliance: Transparent and explainable model decisions

Technical Details

Dependencies

• pandas: Data manipulation and analysis
• numpy: Numerical computing
• scikit-learn: Machine learning algorithms
• xgboost: Gradient boosting framework
• imbalanced-learn: Handling imbalanced datasets
• matplotlib/seaborn: Data visualization
• joblib: Model serialization

Model Pipeline

1. Data loading and exploration
2. Data cleaning and preprocessing
3. Feature engineering
4. Train/test split with stratification
5. Feature scaling
6. Class imbalance handling (SMOTE)
7. Model training (3 algorithms)
8. Model evaluation and comparison
9. Hyperparameter tuning for best model
10. Feature importance analysis
11. Model persistence

Future Enhancements

1. Model Monitoring: Track performance over time
2. Ensemble Methods: Combine multiple models for better performance
3. Feature Selection: Automated feature selection techniques
4. Real-time Predictions: API development for live predictions
5. Model Explainability: SHAP values for individual predictions
6. A/B Testing: Compare model versions in production

Troubleshooting

Common Issues

1. Missing Dependencies:

   pip install --upgrade -r requirements.txt

2. Memory Issues: Reduce dataset size or use sampling for large datasets

3. Jupyter Kernel Issues:

   python -m ipykernel install --user --name=loan_prediction

4. XGBoost Installation Issues:

   conda install -c conda-forge xgboost

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is for educational and research purposes.

Contact

For questions or issues, please refer to the notebook documentation or create an issue in the repository.