🏦 Loan Approval Prediction System

An end-to-end machine learning project that predicts loan approval status based on applicant information using various classification algorithms.

📋 Table of Contents

• Features
• Tech Stack
• Project Structure
• Installation
• Usage
• Deployment
• API Endpoints
• Model Information
• Contributing

✨ Features

• Machine Learning Pipeline: Complete ML pipeline with data ingestion, transformation, and model training
• Multiple Algorithms: Compares Random Forest, Decision Tree, Gradient Boosting, Logistic Regression, AdaBoost, and SVC
• Web Interface: User-friendly Flask web application for predictions
• RESTful API: Health check and prediction endpoints
• Docker Support: Containerized application for easy deployment
• Azure Deployment: CI/CD pipeline configured for Azure Web Apps
• Logging: Comprehensive logging for debugging and monitoring
• Exception Handling: Custom exception handling throughout the application

🛠 Tech Stack

• Python 3.11+
• Flask - Web framework
• scikit-learn - Machine learning algorithms
• pandas & numpy - Data manipulation
• Gunicorn - WSGI HTTP Server
• Docker - Containerization
• Azure Web Apps - Cloud deployment

📁 Project Structure

ML_Project/
├── src/
│   ├── Components/
│   │   ├── data_ingestion.py      # Data loading and splitting
│   │   ├── data_transformation.py  # Feature engineering
│   │   └── model_trainer.py        # Model training and evaluation
│   ├── Pipeline/
│   │   ├── predict_pipeline.py     # Prediction pipeline
│   │   └── training_pipeline.py    # Training orchestration
│   ├── Exception.py                # Custom exception handling
│   ├── Logger.py                   # Logging configuration
│   └── utils.py                    # Utility functions
├── templates/
│   ├── index.html                  # Input form
│   └── home.html                   # Results page
├── artifacts/                      # Trained models and data
├── logs/                          # Application logs
├── app.py                         # Flask application
├── Dockerfile                     # Docker configuration
├── requirements.txt               # Python dependencies
└── setup.py                       # Package setup

🚀 Installation

Local Setup

1. Clone the repository

git clone <your-repo-url>
cd ML_Project

2. Create virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run the application

python app.py

The application will be available at http://localhost:5000

Docker Setup

1. Build the Docker image

docker build -t loan-approval-prediction .

2. Run the container

docker run -p 8000:8000 loan-approval-prediction

The application will be available at http://localhost:8000

💻 Usage

Web Interface

1. Navigate to http://localhost:5000/loan_prediction
2. Fill in the loan application form with:
   • Loan ID
   • Number of Dependents
   • Annual Income
   • Loan Amount
   • Loan Term (months)
   • CIBIL Score
   • Asset Values (Residential, Commercial, Luxury, Bank)
   • Education Level
   • Self-Employment Status
3. Click "Predict" to get the loan approval prediction

Training the Model

To retrain the model with new data:

python src/Components/data_ingestion.py

This will:

1. Load the dataset
2. Split into train/test sets
3. Transform the data
4. Train multiple models
5. Save the best model to artifacts/

🌐 Deployment

Docker Deployment

# Build
docker build -t loan-approval-prediction .

# Run
docker run -d -p 8000:8000 --name loan-app loan-approval-prediction

# Check logs
docker logs loan-app

# Stop
docker stop loan-app

Azure Web Apps

The project includes a GitHub Actions workflow (.github/workflows/main_loanapprovalprediction.yml) for automatic deployment to Azure:

1. Push to the main branch
2. GitHub Actions will automatically build and deploy
3. Access your app at: https://loanapprovalprediction.azurewebsites.net

Other Cloud Platforms

AWS Elastic Beanstalk:

eb init -p python-3.11 loan-approval-app
eb create loan-approval-env
eb deploy

Google Cloud Run:

gcloud run deploy loan-approval --source . --platform managed --region us-central1

Heroku:

heroku create loan-approval-prediction
git push heroku main

🔌 API Endpoints

Health Check

GET /health

Response:

{
  "status": "healthy",
  "service": "loan-approval-prediction"
}

Home

GET /

Returns welcome message

Loan Prediction Form

GET /loan_prediction

Returns the prediction form

Make Prediction

POST /loan_prediction
Content-Type: application/x-www-form-urlencoded

loan_id=1234&no_of_dependents=2&income_annum=500000&...

🤖 Model Information

Features Used

Numerical Features:

• loan_id, no_of_dependents, income_annum, loan_amount, loan_term
• cibil_score, residential_assets_value, commercial_assets_value
• luxury_assets_value, bank_asset_value

Categorical Features:

• education (Graduate/Not Graduate)
• self_employed (Yes/No)

Models Evaluated

1. Random Forest Classifier
2. Decision Tree Classifier
3. Gradient Boosting Classifier
4. Logistic Regression
5. AdaBoost Classifier
6. Support Vector Classifier (SVC)

The best performing model (accuracy > 90%) is automatically selected and saved.

Preprocessing

• Numerical: Median imputation + Standard scaling
• Categorical: Most frequent imputation + One-hot encoding + Scaling
• Target: Label encoding (Approved/Rejected)

📊 Performance

The model achieves:

• Accuracy: > 90% on test set
• ROC-AUC: Calculated for models supporting probability estimates

🐛 Troubleshooting

Issue: Module not found

pip install -r requirements.txt

Issue: Port already in use

# Change port in app.py or use different port
python app.py  # Modify the port in the file

Issue: Model files missing

# Retrain the model
python src/Components/data_ingestion.py

📝 License

This project is licensed under the MIT License.

👤 Author

Hammad Ali Tahir

• Email: hammadalitahir8@gmail.com

🤝 Contributing

Contributions, issues, and feature requests are welcome!

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

📞 Support

For support, email hammadalitahir8@gmail.com or open an issue in the repository.

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Note: Make sure to place your dataset at src/Notebook/data/loan_approval_dataset.csv before training the model.