An end-to-end Full Stack Machine Learning Web Application that predicts diseases based on user-selected symptoms and provides possible medicine recommendations.
This project uses a Random Forest Classifier for disease prediction, a FastAPI backend for ML model serving, a MongoDB database for storing records, and a frontend built with HTML & CSS for user interaction.
- ✅ Users can select multiple symptoms from the interface
- ✅ Random Forest ML model predicts the most probable disease
- ✅ Provides basic medicine recommendations for the predicted disease
- ✅ FastAPI backend to handle ML model and API requests
- ✅ MongoDB database to store user inputs & prediction history
- ✅ Simple and responsive UI with HTML & CSS
- Machine Learning: Python, Random Forest (Scikit-learn, Pandas, NumPy)
- Backend: FastAPI
- Frontend: HTML, CSS
- Database: MongoDB
- Others: Uvicorn, Git