Rental Price Prediction API

Overview

This microservice provides real-time rental price predictions through a RESTful API interface. Built with a production-grade MLOps architecture, it transforms a machine learning model into a scalable, maintainable API service.

Architecture

The application follows a modular microservice architecture:

app/
├── api/               # API endpoint definitions
│   ├── prediction.py  # Prediction endpoints
│   
├── config/            # Configuration management
│   ├── __init__.py
│   ├── logger.py
│   └── model.py
├── logs/              # Application logs
├── model/             # ML model artifacts
├── schema/            # Data validation schemas
│   └── apartment.py
└── services/          # Business logic layer
    ├── model_inference.py
    └── run.py

Technical Stack

• Framework: Flask
• ML Model Serving: Custom inference service
• Data Validation: Pydantic

Development Tools

• Poetry (Dependency Management)
• Ruff (Code Quality)
• GitHub Actions (CI/CD)
• Make (Automation)

Prerequisites

• Python 3.9+
• Poetry
• Make (optional)

Installation

Clone the repository:

git clone https://github.com/Mohammed-abdulaziz-eisa/Rental-Price-Prediction-App-to-Micro-Transformation-API-Development.git
cd Rental Price Prediction App to Micro Transformation API Development

Install dependencies:

poetry install

Configure environment:

cp .env.example .env
# Edit .env with your configurations

Usage

Start the API server:

poetry run python3 run.py

or

make runner
# it will install dependencies and run the server

Make predictions:

curl -X POST http://localhost:5000/predict \
  -H "Content-Type: application/json" \
  -d '{
    "area": 85,
    "construction_year": 2015,
    "bedrooms": 2,
    "garden_area": 20,
    "balcony_area": 1,
    "parking_present": 1,
    "furnished": 0,
    "garage_present": 0,
    "storage_present": 1
  }'

API Endpoints

GET /pred

Predicts rental price based on apartment features.

Request Body Schema:

{
    "area": int,
    "bedrooms": int,
    // Additional features as defined in schema/apartment.py
}

Response:

{
    "predicted_price": float,
}

Development

Code Quality

The project uses Ruff for code formatting and linting:

poetry run ruff format .
poetry run ruff check .

# also you can use flake8 for linting 
poetry run flake8 .

Testing

poetry run pytest

Logging

Logs are stored in app/logs/app.log with the following levels:

• INFO: General application flow
• WARNING: Unexpected but handled situations
• ERROR: Application errors requiring attention
• DEBUG: Detailed information for development

Deployment

The application is containerized and can be deployed using Docker:

docker build -t rental-price-api .
docker run -p 5000:5000 rental-price-api

Monitoring

The application includes basic monitoring endpoints:

• /health: API health check
• /metrics: Basic performance metrics

Contributing

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

Contact

Mohamed-abdulaziz-eisa - [mohamed.abdulaziz.eisa@gmail.com]