🚀 Machine Learning Public Health Dashboard

📌 Project Overview

This project is a Machine Learning-powered Public Health Dashboard that provides COVID-19 data analysis and infectious rate predictions using FastAPI (Python) for the backend and Next.js (React) for the frontend.

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🏗 Tech Stack

Frontend:

• 🟢 Next.js (React + TypeScript)
• 🎨 Tailwind CSS (for UI styling)
• 🔄 Axios (for API calls)

Backend:

• 🐍 FastAPI (Python)
• 📊 Scikit-learn (Machine Learning)
• 🔥 Uvicorn (ASGI server)

Deployment:

• 🐳 Docker & Docker Compose
• 🌎 Vercel (Frontend)
• 🛠 AWS/GCP (Backend, optional)

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🛠 Installation & Setup

1️⃣ Clone the Repository

  git clone https://github.com/your-username/machine-learning-project.git
  cd machine-learning-project

2️⃣ Setup the Backend (FastAPI)

cd backend
python -m venv venv
source venv/bin/activate  # (Windows: venv\Scripts\activate)
pip install -r requirements.txt
uvicorn main:app --host 0.0.0.0 --port 8080 --reload

🔹 API will be running at: http://127.0.0.1:8080

3️⃣ Setup the Frontend (Next.js)

cd ../frontend
npm install
npm run dev

🔹 App will be available at: http://127.0.0.1:3000

4️⃣ Run with Docker (Optional)

docker-compose up --build

🔹 This will start both backend (8080) and frontend (3000) together.

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🔥 Features

✅ View COVID-19 Data (cases, deaths, vaccinations, etc.) ✅ Predict Infection Rate based on user input ✅ Machine Learning-powered infectious rate estimation ✅ Modern & responsive UI with Tailwind CSS ✅ REST API for data retrieval & predictions ✅ Deployed using Docker & Cloud Services

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🔌 API Endpoints

1️⃣ Get COVID-19 Data

Endpoint: GET /covid-data/

{
  "covid_stats": [
    { "country": "USA", "cases": 1000000, "deaths": 50000, "hospitalizations": 20000, "vaccinations": 900000 }
  ]
}

2️⃣ Predict Infection Rate

Endpoint: POST /predict-infection-rate/

Request:

{
  "country": "USA",
  "population": 331000000,
  "confirmed_cases": 1000000,
  "deaths": 50000,
  "vaccinations": 900000
}

Response:

{
  "predicted_infection_rate": 2.45
}

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📜 Folder Structure

machine-learning-project/
│── backend/             # FastAPI Backend
│   ├── main.py          # API Server
│   ├── models.py        # ML Models
│   ├── routes.py        # API Endpoints
│   ├── requirements.txt # Dependencies
│── frontend/            # Next.js Frontend
│   ├── app/             # Pages & Components
│   ├── lib/api.ts       # API Calls to Backend
│── docker-compose.yml   # Docker Setup
│── README.md            # Documentation

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📌 Deployment

Deploy Backend to AWS/GCP (Example using Docker)

docker build -t fastapi-ml .
docker run -p 8080:8080 fastapi-ml

Deploy Frontend to Vercel

cd frontend
vercel deploy

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🚀 Contributing

1. Fork the repository
2. Create a new branch (feature-xyz)
3. Commit your changes (git commit -m "Added new feature")
4. Push to GitHub & create a Pull Request

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📝 License

This project is MIT licensed. Feel free to modify and distribute.

💡 Happy Coding! 🚀