Credit Score Prediction API

A Flask-based REST API service that provides credit score predictions using a machine learning model. This service is containerized with Docker and designed to work within a microservices architecture.

🚀 Features

• Credit Score Prediction: Predict credit scores based on personal and financial data
• RESTful API: Simple HTTP endpoints for easy integration
• Dockerized: Fully containerized for easy deployment
• CORS Enabled: Cross-origin resource sharing for web applications
• Health Check: Built-in health check endpoint

📋 Prerequisites

• Python 3.10+
• Docker and Docker Compose
• Pre-trained ML model (model.pkl)

🛠️ Installation

Local Development

1. Clone the repository:

git clone <repository-url>
cd pt2-ai

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

python app.py

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

Docker Deployment

1. Build and run with Docker Compose:

docker-compose up --build

The service will be available at http://localhost:5001

📡 API Endpoints

Health Check

GET /

Returns: "Healthy"

Predict Credit Score

POST /predict
Content-Type: application/json

Request Body

{
  "FLAG_OWN_CAR": 1,
  "FLAG_OWN_REALTY": 0,
  "CNT_CHILDREN": 2,
  "AMT_INCOME_TOTAL": 50000,
  "NAME_INCOME_TYPE": "Working",
  "NAME_EDUCATION_TYPE": "Higher education",
  "NAME_FAMILY_STATUS": "Married",
  "NAME_HOUSING_TYPE": "House / apartment",
  "DAYS_BIRTH": -12000,
  "DAYS_EMPLOYED": -3000,
  "OCCUPATION_TYPE": "Accountants",
  "CNT_FAM_MEMBERS": 4,
  "CNT_ADULTS": 2,
  "AMT_INCOME_PER_CHILDREN": 25000,
  "AMT_INCOME_PER_FAM_MEMBER": 12500
}

Response

{
  "score": 85
}

📊 Input Parameters

Parameter	Type	Description
FLAG_OWN_CAR	Integer	Car ownership flag (0/1)
FLAG_OWN_REALTY	Integer	Real estate ownership flag (0/1)
CNT_CHILDREN	Integer	Number of children
AMT_INCOME_TOTAL	Float	Total income amount
NAME_INCOME_TYPE	String	Type of income source
NAME_EDUCATION_TYPE	String	Education level
NAME_FAMILY_STATUS	String	Family status
NAME_HOUSING_TYPE	String	Housing type
DAYS_BIRTH	Integer	Days since birth (negative value)
DAYS_EMPLOYED	Integer	Days employed (negative value)
OCCUPATION_TYPE	String	Occupation type
CNT_FAM_MEMBERS	Integer	Number of family members
CNT_ADULTS	Integer	Number of adults in family
AMT_INCOME_PER_CHILDREN	Float	Income per child
AMT_INCOME_PER_FAM_MEMBER	Float	Income per family member

🏗️ Project Structure

pt2-ai/
├── app.py                 # Main application entry point
├── config.py             # Configuration and model loading
├── controller.py         # Business logic for predictions
├── routes.py             # API route definitions
├── model.pkl             # Pre-trained ML model
├── requirements.txt      # Python dependencies
├── Dockerfile           # Docker container configuration
├── docker-compose.yaml  # Docker Compose configuration
└── README.md           # Project documentation

🐳 Docker Configuration

The application is configured to run in a Docker container with the following specifications:

• Base Image: Python 3.10
• Port Mapping: 5001 (host) → 5000 (container)
• Environment: Flask debug mode enabled
• Network: Connected to pt2-backend_network (external)

🔧 Configuration

Environment Variables

• FLASK_APP: Set to app.py
• FLASK_RUN_HOST: Set to 0.0.0.0 for container access
• FLASK_ENV: Set to debug for development

CORS Configuration

The API is configured to accept requests from any origin with GET and POST methods enabled.

🧪 Testing

Example cURL Request

curl -X POST http://localhost:5001/predict \
  -H "Content-Type: application/json" \
  -d '{
    "FLAG_OWN_CAR": 1,
    "FLAG_OWN_REALTY": 0,
    "CNT_CHILDREN": 2,
    "AMT_INCOME_TOTAL": 50000,
    "NAME_INCOME_TYPE": "Working",
    "NAME_EDUCATION_TYPE": "Higher education",
    "NAME_FAMILY_STATUS": "Married",
    "NAME_HOUSING_TYPE": "House / apartment",
    "DAYS_BIRTH": -12000,
    "DAYS_EMPLOYED": -3000,
    "OCCUPATION_TYPE": "Accountants",
    "CNT_FAM_MEMBERS": 4,
    "CNT_ADULTS": 2,
    "AMT_INCOME_PER_CHILDREN": 25000,
    "AMT_INCOME_PER_FAM_MEMBER": 12500
  }'

📦 Dependencies

Core Dependencies

• Flask 3.1.1: Web framework
• scikit-learn 1.4.2: Machine learning library
• pandas 2.3.1: Data manipulation
• joblib 1.5.1: Model serialization
• flask-cors 6.0.1: Cross-origin resource sharing
• numpy 2.2.6: Numerical computing

Full dependency list available in requirements.txt

🤝 Contributing

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

📄 License

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

🆘 Support

For support and questions, please open an issue in the repository or contact the development team.

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Note: Make sure you have the model.pkl file in the root directory before running the application. This file contains the pre-trained machine learning model required for predictions.