housing-price-api

🏡 California Housing Price Prediction API

A production-ready Machine Learning service that predicts median housing prices in California districts based on census data. This project demonstrates an end-to-end MLOps workflow: from model training to deployment as a containerized REST API.

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🚀 Key Features

• Random Forest Regressor: Trained on the standard California Housing dataset.
• REST API: Built with FastAPI for high performance and automatic documentation.
• Containerized: Fully Dockerized for consistent deployment across any environment.
• Input Validation: Uses Pydantic models to ensure data integrity.

🛠️ Tech Stack

• Language: Python 3.9+
• Framework: FastAPI, Uvicorn
• ML Libraries: Scikit-Learn, Pandas, Joblib
• Containerization: Docker

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📦 Installation & Setup

You can run this project either locally with Python or as a Docker container.

Option 1: Run with Docker (Recommended)

Build the image and run the container in two simple steps:

# 1. Build the Docker image
docker build -t housing-price-api .

# 2. Run the container
docker run -p 8000:8000 housing-price-api

Option 2: Run Locally

If you prefer to run it without Docker:

# 1. Install dependencies
pip install -r requirements.txt

# 2. Train the model (creates the .joblib file)
python train.py

# 3. Start the API server
uvicorn main:app --reload

📡 API Usage

Once the server is running, you can interact with the API endpoints.

📖 Interactive Documentation

Visit http://localhost:8000/docs to see the Swagger UI. You can test the endpoints directly from your browser!

🔮 Prediction Endpoint

POST /predict

Send a JSON body with the following features:

{
  "MedInc": 8.3252,
  "HouseAge": 41.0,
  "AveRooms": 6.9841,
  "AveBedrms": 1.0238,
  "Population": 322.0
}

Response:

{
  "predicted_median_house_value": 4.152
}

📂 Project Structure

housing-price-api/
├── Dockerfile          
├── main.py             
├── train.py            
├── requirements.txt    
└── README.md