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🏥 InsurePredict – Insurance Premium Prediction System

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.


🚀 Live Demo

🌐 Application: http://52.5.22.207:8000/

📌 API Docs (Swagger): http://52.5.22.207:8000/docs


📌 Features

  • 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.

🛠 Tech Stack

Frontend

  • HTML5
  • CSS3
  • JavaScript

Backend

  • FastAPI
  • Uvicorn

Machine Learning

  • Python
  • Scikit-learn
  • Pandas
  • NumPy
  • Joblib

Deployment

  • Docker
  • Docker Hub
  • AWS EC2

📊 Workflow

User Input
     │
     ▼
Data Validation
     │
     ▼
Preprocessing Pipeline
     │
     ▼
Trained ML Model
     │
     ▼
Insurance Premium Prediction
     │
     ▼
Display Result

📂 Project Structure

Insurance-Premium-Prediction-System/
│
├── artifacts/
├── data/
├── models/
├── static/
├── templates/
├── app.py
├── prediction_pipeline.py
├── requirements.txt
├── Dockerfile
├── .dockerignore
├── README.md
└── LICENSE

⚙️ Installation

Clone the repository

git clone https://github.com/mayaannkkk/Insurance-Premium-Prediction-System.git

cd Insurance-Premium-Prediction-System

Create Virtual Environment

python -m venv myenv

Activate Environment

Windows

myenv\Scripts\activate

Linux/Mac

source myenv/bin/activate

Install Dependencies

pip install -r requirements.txt

▶️ Run the Application

uvicorn app:app --reload

Open

http://127.0.0.1:8000

🐳 Docker

Build Image

docker build -t insurance-premium-api .

Run Container

docker run -d -p 8000:8000 insurance-premium-api

☁️ AWS Deployment

The application is deployed on an AWS EC2 instance using Docker.


🎯 Future Improvements

  • User Authentication
  • Premium Comparison Across Companies
  • Model Monitoring
  • CI/CD Pipeline using GitHub Actions
  • Kubernetes Deployment
  • HTTPS with Nginx
  • Cloud Monitoring

👨‍💻 Author

Mayank Goyal

LinkedIn: https://www.linkedin.com/in/mayank-goyal-4a419228b/

About

An end-to-end Machine Learning web application that predicts health insurance premiums using FastAPI, Scikit-learn, Docker, and AWS EC2 deployment.

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