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https://github.com/falakrana/disease-prediction-using-ml

This project is an AI-powered web application that predicts diseases based on user-input symptoms. It uses Machine Learning algorithms like Random Forest, Decision Tree, and Naïve Bayes to provide accurate predictions. The system features a Flask backend, a React.js frontend, and ensures user privacy by not storing searches.
https://github.com/falakrana/disease-prediction-using-ml

decision-trees flask ml naive-bayes-classifier randomforestclassifier reactjs svm-classifier

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This project is an AI-powered web application that predicts diseases based on user-input symptoms. It uses Machine Learning algorithms like Random Forest, Decision Tree, and Naïve Bayes to provide accurate predictions. The system features a Flask backend, a React.js frontend, and ensures user privacy by not storing searches.

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README

          

# Disease Prediction System

## Overview
The **Disease Prediction System** is a web-based application that predicts diseases based on user-input symptoms using Machine Learning. The system consists of a **Flask backend** for handling the ML model and API requests and a **React frontend** for user interaction.

## Tech Stack
- **Frontend:** React.js, Vite, HTML, CSS, JavaScript
- **Backend:** Flask (Python)
- **Machine Learning:** Scikit-learn, Pandas, NumPy
- **Database:** MongoDB Atlas

## Folder Structure
```
├── backend
│ ├── models/ # ML models
│ ├── utils/ # Helper functions
│ ├── .env.example # Environment variable example
│ ├── .gitignore # Ignore unnecessary files
│ ├── app.py # Main Flask application
│ ├── README.md # Backend documentation
│ ├── requirements.txt # Python dependencies

├── frontend
│ ├── node_modules/ # Dependencies
│ ├── public/ # Static assets
│ ├── src/ # React source code
│ ├── .gitignore # Ignore unnecessary files
│ ├── eslint.config.js # Linting configuration
│ ├── index.html # Main HTML file
│ ├── package.json # Frontend dependencies
│ ├── package-lock.json # Package lock file
│ ├── README.md # Frontend documentation
│ ├── vite.config.js # Vite configuration
```

## Installation
### **1. Clone the Repository**
```sh
git clone https://github.com/your-username/disease-prediction.git
cd disease-prediction
```

### **2. Backend Setup**
```sh
cd backend
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
```
Run the backend server:
```sh
python app.py
```

### **3. Frontend Setup**
```sh
cd frontend
npm install
npm run dev
```

## Usage
1. Open the frontend in the browser.
2. Enter symptoms and submit.
3. The backend processes input and returns predicted diseases.

## Future Enhancements
- Improve ML model accuracy.
- Implement user authentication.

## Contributing
Feel free to fork this repository and submit pull requests.