--ONGOING--
InsuranSure is a full-stack, microservices-based web application designed to demystify insurance costs. It provides users with instant, data-driven estimates for various insurance premiums, starting with medical insurance and expanding to other domains.
This project is not just a prediction tool; it's a complete end-to-end demonstration of a modern web application, integrating a machine learning backend with a robust web interface.
⦿ Multi-Linear Regression Model: Utilizes a carefully trained model to predict medical insurance premiums based on key user inputs.
⦿ RESTful API Architecture: Built with a decoupled frontend and backend, featuring a Node.js gateway that communicates with a Python/Flask ML microservice.
⦿ Interactive UI: A clean, responsive, and user-friendly interface built with React that allows for seamless data entry and clear presentation of results.
⦿ "What If" Scenarios: Instantly see how changes in lifestyle or demographics (e.g., quitting smoking) can affect your estimated premium.
⦿ (Planned) User Accounts: Secure user authentication (JWT) to save prediction history and track estimates over time.
⦿ (Planned) Multi-Insurance Support: A scalable architecture ready to incorporate models for car, life, and home insurance.
This project is built using a microservice architecture to ensure separation of concerns, scalability, and maintainability.
⦿ Frontend (React): The client-side application that users interact with. It is responsible for capturing user input and displaying results. It only communicates with the Backend Gateway.
⦿ Backend Gateway (Node.js/Express): The central hub of the application. It handles core business logic, user authentication, and acts as a single point of contact for the frontend. It forwards ML-specific requests to the appropriate microservice.
⦿ ML Microservice (Python/Flask): A dedicated, lightweight service whose sole responsibility is to serve predictions from the trained machine learning model via a REST API.
⦿ [x] Develop and train the core Medical Insurance Prediction model.
⦿ [x] Build the Python/Flask microservice for the model.
⦿ [x] Set up the Node.js gateway and connect it to the Flask service.
⦿ [x] Develop the complete React user interface.
⦿ [x] Implement JWT-based user authentication and database integration.
⦿ [x] Add a "Prediction History" feature for logged-in users.
⦿ [ ] Research and integrate a Car Insurance Prediction model.
⦿ [ ] Research and integrate a Life Insurance Prediction model.
⦿ [ ] Containerize the application with Docker for easier deployment.