🛡️ Insurance Prediction Backend (FastAPI)

A scalable and high‑performance FastAPI backend service that exposes REST APIs for predicting insurance premiums using a trained machine learning model. This backend is designed to integrate seamlessly with frontend applications (React, mobile apps) or other services.

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🚀 Features

• ⚡ Built with FastAPI for speed and modern API development
• 🤖 Machine Learning–powered insurance premium prediction
• 📄 Automatic API documentation with Swagger UI and ReDoc
• 🧩 Clean, modular project structure
• 🔌 Easy integration with frontend and external systems
• ☁️ Ready for cloud deployment

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📁 Project Structure

insurance-prediction-backend-fastapi/
├── model/                  # Saved trained ML models
├── model_train/            # Model training and preprocessing scripts
├── app.py                  # FastAPI application entry point
├── database.py             # Database configuration (optional / extensible)
├── models.py               # Pydantic / ORM models
├── requirements.txt        # Python dependencies
├── .gitignore
└── README.md

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🧠 Tech Stack

Layer	Technology
Backend Framework	FastAPI
Language	Python 3.8+
Server	Uvicorn
ML Library	scikit-learn
API Docs	Swagger UI / ReDoc

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📌 API Endpoints

Once the server is running, FastAPI provides interactive documentation automatically:

Purpose	URL
Swagger UI	/docs
ReDoc	/redoc
Base URL	/

🔮 Prediction Endpoint

POST /predict

Sample Request

{
  "age": 45,
  "bmi": 28.0,
  "children": 2,
  "smoker": "yes",
  "gender": "male",
  "region": "southwest"
}

Sample Response

{
  "predicted_premium": 12345.67
}

⚠️ Input fields depend on the trained model. Update request schema as per your implementation.

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🛠️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/harinandanmv/insurance-prediction-backend-fastapi.git
cd insurance-prediction-backend-fastapi

2️⃣ Create a Virtual Environment

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

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the Server

uvicorn app:app --reload

Server will start at:

http://127.0.0.1:8000

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🧪 Testing the API

You can test the backend using:

• Swagger UI (/docs)
• ReDoc (/redoc)
• Postman / Insomnia
• curl

Example curl Request

curl -X POST "http://127.0.0.1:8000/predict" \
     -H "Content-Type: application/json" \
     -d '{"age":30,"bmi":26,"children":1,"smoker":"no","gender":"female","region":"northwest"}'

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🧾 Model Training

The model_train/ directory contains scripts used to:

• Load and preprocess the dataset
• Train the insurance prediction model
• Save the trained model to the model/ directory

This separation allows independent experimentation and model updates without affecting the API layer.

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☁️ Deployment

This backend can be deployed using:

• Docker
• AWS / GCP / Azure
• Render / Railway / Fly.io
• On‑premise servers

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a new branch
3. Commit your changes
4. Open a Pull Request

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📜 License

This project is licensed under the MIT License.

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⭐ If you find this project useful, consider giving it a star!