🏦 Home Credit Default Risk Prediction

🎯 Business Problem

Many individuals struggle to access loans due to:

• Insufficient credit history
• Non-existent credit records
• Vulnerability to predatory lenders

Home Credit Group's Mission: Expand financial inclusion for the unbanked population by:

• Providing safe borrowing experiences
• Using alternative data for credit assessment
• Ensuring fair loan approval processes

📊 Data Infrastructure

Core Datasets

Dataset	Description	Size	Key Features
application_{train|test}.csv	Static application data	500K+ rows	Demographic, financial info
bureau.csv	Credit Bureau records	1.7M+ rows	Previous credits history
bureau_balance.csv	Monthly Credit Bureau data	27M+ rows	Credit status changes
POS_CASH_balance.csv	Point of Sale/Cash loans data	10M+ rows	Monthly loan snapshots
credit_card_balance.csv	Credit card history	3.8M+ rows	Card usage patterns
previous_application.csv	Previous loan applications	1.6M+ rows	Application history
installments_payments.csv	Repayment history	13M+ rows	Payment patterns

🚀 Deployment Options

1️⃣ Local Development

# Setup virtual environment
python -m venv venv
source venv/bin/activate  # Unix/macOS
.\venv\Scripts\activate   # Windows

# Install & Train
pip install -r requirements.txt
python model_training/src/model_training.py

# Launch Application
python credit_fraud_app/app.py

2️⃣ Docker Deployment (Recommended)

# Full Stack Deployment
docker-compose up --build

# Individual Components
# Model Training
cd model_training
docker build -t model-trainer .
docker run -v "$(pwd)/models:/app/models" model-trainer

# Web Application
cd credit_fraud_app
docker build -t credit-fraud-app .
docker run -p 5001:5000 credit-fraud-app

3️⃣ CI/CD Pipeline (Production)

graph LR
    A[Code Push] --> B[Jenkins Pipeline]
    B --> C[Automated Tests]
    C --> D[Docker Build]
    D --> E[Security Scan]
    E --> F[Deploy]
    F --> G[Health Check]
    G --> H[Notifications]

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🏗️ Architecture

HOME-CREDIT-DEFAULT-RISK/
├── 🔮 model_training/          # ML Pipeline
│   ├── src/
│   │   ├── model_training.py   # Training logic
│   │   └── model_testing.py    # Testing suite
│   ├── data/                   # Data storage
│   ├── models/                 # Model artifacts
│   └── Dockerfile             
├── 🌐 credit_fraud_app/        # Web Service
│   ├── app/
│   │   ├── static/            # Frontend assets
│   │   ├── templates/         # UI templates
│   │   └── models/           # Model deployment
│   └── Dockerfile
├── 🔄 jenkins/                 # CI/CD Config
└── 🐳 docker-compose.yml      # Orchestration

📊 Model Performance

Metric	Training	Testing
Accuracy	89.06%	88.96%
Precision	22.14%	21.44%
Recall	14.06%	13.98%
ROC AUC	54.86%	54.75%

📈 Performance Analysis

import seaborn as sns
import matplotlib.pyplot as plt

# ROC Curve Visualization
plt.figure(figsize=(10, 6))
plt.plot(fpr, tpr, label=f'ROC (AUC = {roc_auc:.3f})')
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend()
plt.show()

🛠️ Quick Commands

Docker Operations

# Build Services
docker-compose build

# Launch Stack
docker-compose up -d

# Check Status
docker-compose ps

# View Logs
docker-compose logs -f

# Shutdown
docker-compose down

Model Training

# Local Training
python model_training.py \
    --train-path data/application_train.csv \
    --test-path data/application_test.csv \
    --model-output models/credit_model.pkl

# Containerized Training
docker run -v $(pwd)/data:/app/data \
           -v $(pwd)/models:/app/models \
           model-trainer

🔄 CI/CD Pipeline Features

• Automated Testing: Unit tests, integration tests
• Quality Checks: Code style, complexity
• Security: Trivy scanning, dependency checks
• Deployment: Blue-green deployment strategy
• Monitoring: Health checks, performance metrics
• Notifications: Slack integration

📝 Development Notes

1. Data Preprocessing

   • Handle missing values
   • Feature engineering
   • Scaling/normalization

2. Model Training

   • XGBoost implementation
   • Hyperparameter optimization
   • Cross-validation

3. Deployment

   • Model serialization
   • API endpoint creation
   • Load balancing setup

🤝 Contributing

1. Fork the repository
2. Create feature branch

   git checkout -b feature/amazing-feature

3. Commit changes

   git commit -m 'Add amazing feature'

4. Push to branch

   git push origin feature/amazing-feature

5. Create Pull Request

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🏦 Empowering Financial Inclusion Through Technology 🚀