An end-to-end Machine Learning web application that predicts the estimated health insurance premium based on a user's demographic, lifestyle, and medical information. The application is built using FastAPI, containerized with Docker, and deployed on AWS EC2 for real-time predictions.
🌐 Application: http://52.5.22.207:8000/
📌 API Docs (Swagger): http://52.5.22.207:8000/docs
- Predicts health insurance premiums in real time.
- User-friendly and responsive interface.
- FastAPI backend for high-performance API responses.
- Machine Learning prediction pipeline using Scikit-learn.
- Input validation and preprocessing.
- Dockerized application.
- Cloud deployment on AWS EC2.
- HTML5
- CSS3
- JavaScript
- FastAPI
- Uvicorn
- Python
- Scikit-learn
- Pandas
- NumPy
- Joblib
- Docker
- Docker Hub
- AWS EC2
User Input
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Data Validation
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Preprocessing Pipeline
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Trained ML Model
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Insurance Premium Prediction
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Display Result
Insurance-Premium-Prediction-System/
│
├── artifacts/
├── data/
├── models/
├── static/
├── templates/
├── app.py
├── prediction_pipeline.py
├── requirements.txt
├── Dockerfile
├── .dockerignore
├── README.md
└── LICENSE
git clone https://github.com/mayaannkkk/Insurance-Premium-Prediction-System.git
cd Insurance-Premium-Prediction-Systempython -m venv myenvWindows
myenv\Scripts\activateLinux/Mac
source myenv/bin/activatepip install -r requirements.txtuvicorn app:app --reloadOpen
http://127.0.0.1:8000
docker build -t insurance-premium-api .docker run -d -p 8000:8000 insurance-premium-apiThe application is deployed on an AWS EC2 instance using Docker.
- User Authentication
- Premium Comparison Across Companies
- Model Monitoring
- CI/CD Pipeline using GitHub Actions
- Kubernetes Deployment
- HTTPS with Nginx
- Cloud Monitoring
Mayank Goyal
LinkedIn: https://www.linkedin.com/in/mayank-goyal-4a419228b/