Credit Risk Identifier 🏦

A comprehensive machine learning web application for credit risk assessment built with Flask and advanced ML models.

🚀 Overview

This project provides an end-to-end solution for credit risk prediction using machine learning. It features a user-friendly web interface built with Flask that allows financial institutions to assess loan default risk in real-time.

Key Features

• Champion ML Model: Gradient Boosting Classifier achieving 91.90% accuracy and 91.85% AUC
• Advanced Feature Engineering: Binning + Encoding approach for optimal performance
• 5-Tier Risk Assessment: Low, Medium-Low, Medium, Medium-High, High Risk
• Interactive Web Interface: Bootstrap-powered responsive design
• Business Recommendations: Actionable insights for each risk level
• Real-time Predictions: API endpoints for integration
• Comprehensive Testing: Full test suite included

📊 Model Performance

• Accuracy: 91.90%
• AUC Score: 91.85%
• Cross-Validation Score: 91.67%
• Feature Engineering: Binning + Categorical Encoding
• Model Type: Gradient Boosting Classifier (No PCA)

🏗️ Project Structure

Credit-Risk-Identifier/
│
├── Web_App/                          # Flask Web Application
│   ├── app.py                        # Main Flask application
│   ├── requirements.txt              # Python dependencies
│   ├── train_champion_model.py       # Model training script
│   ├── test_app.py                   # Test suite
│   ├── README.md                     # Web app documentation
│   └── templates/
│       ├── index.html               # Input form
│       └── result.html              # Results display
│
├── credit-risk-dataset/              # Dataset
│   └── credit_risk_dataset.csv      # Main dataset (32,581 samples)
│
├── best_model_pipeline.pkl           # Trained model (legacy)
├── modeling_week15_comprehensive.ipynb  # ML development notebook
└── INTEGRATION_SUMMARY.md           # Technical documentation

🚀 Quick Start

Prerequisites

• Python 3.8+
• pip package manager

Installation

1. Clone the repository

   git clone https://github.com/sys0507/Credit-Risk-Identifier.git
   cd Credit-Risk-Identifier

2. Navigate to Web App

   cd Web_App

3. Install dependencies

   pip install -r requirements.txt

4. Train the model (if needed)

   python train_champion_model.py

5. Run the application

   python app.py

6. Access the app Open your browser and go to http://localhost:5000

📋 Input Features

The model requires 11 key features for prediction:

Feature	Description	Type
person_age	Age of the person	Numerical
person_income	Annual income	Numerical
person_emp_length	Employment length (years)	Numerical
person_home_ownership	Home ownership status	Categorical
loan_amnt	Loan amount requested	Numerical
loan_intent	Purpose of the loan	Categorical
loan_int_rate	Interest rate	Numerical
loan_percent_income	Loan as % of income	Numerical
loan_grade	Loan grade	Categorical
cb_person_default_on_file	Historical default	Categorical
cb_person_cred_hist_length	Credit history length	Numerical

🎯 Risk Assessment Levels

Risk Level	Recommendation	Action
Low Risk	✅ APPROVE	Standard processing
Medium-Low Risk	✅ APPROVE	Consider terms adjustment
Medium Risk	⚠️ REVIEW	Additional verification
Medium-High Risk	⚠️ CAUTION	Enhanced due diligence
High Risk	❌ HIGH RISK	Reject or require collateral

🔧 API Endpoints

• GET / - Web interface
• POST /predict - Form submission
• POST /api/predict - JSON API
• GET /health - Health check

API Usage Example

import requests

data = {
    "person_age": 25,
    "person_income": 50000,
    "person_emp_length": 3,
    "person_home_ownership": "RENT",
    "loan_amnt": 10000,
    "loan_intent": "PERSONAL",
    "loan_int_rate": 12.5,
    "loan_percent_income": 0.2,
    "loan_grade": "B",
    "cb_person_default_on_file": "N",
    "cb_person_cred_hist_length": 5
}

response = requests.post("http://localhost:5000/api/predict", json=data)
result = response.json()
print(f"Risk Level: {result['risk_level']}")
print(f"Probability: {result['probability']:.2%}")

🧪 Testing

Run the comprehensive test suite:

python test_app.py

Tests cover:

• Model loading and prediction accuracy
• Form validation and error handling
• API endpoint functionality
• Risk level classification
• Template rendering

📈 Model Development

The champion model was selected from 54 different model variations tested across:

• 3 Feature Engineering Approaches: Raw Features, Log Transform, Binning+Encoding
• 2 PCA Options: With/Without PCA
• 9 ML Algorithms: Logistic Regression, Decision Tree, Random Forest, XGBoost, LGBM, CatBoost, Gradient Boosting, SVM, KNN

See modeling_week15_comprehensive.ipynb for complete analysis.

🛠️ Technical Stack

• Backend: Flask (Python)
• ML Framework: scikit-learn, XGBoost, CatBoost
• Frontend: Bootstrap 5, Chart.js
• Data Processing: pandas, numpy
• Model Serving: pickle, joblib

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📞 Contact

• Author: sys0507
• Repository: Credit-Risk-Identifier
• Issues: Report bugs or request features

🏆 Performance Metrics

Metric	Value
Accuracy	91.90%
Precision	91.23%
Recall	92.15%
F1-Score	91.69%
AUC-ROC	91.85%

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Built with ❤️ for better financial decision making