Placement Prediction Application

A full-stack web application that predicts student placement probability using machine learning. The application consists of a Flask-based REST API backend and a Next.js frontend.

🚀 Features

• Machine Learning Prediction: Uses a trained Random Forest model to predict placement outcomes
• Modern Web Interface: Clean, responsive UI built with Next.js and Tailwind CSS
• Real-time Predictions: Instant results based on student input data
• RESTful API: Well-structured backend API for predictions
• Input Validation: Comprehensive validation for all user inputs
• Error Handling: Graceful error handling and user feedback

📋 Prerequisites

• Python 3.8+ (for backend)
• Node.js 18+ (for frontend)
• npm or yarn (package manager)
• Git (version control)

🏗️ Project Structure

APP/
├── backend/                 # Flask API server
│   ├── src/
│   │   ├── app.py          # Main Flask application
│   │   └── random_forest_model.pkl # Trained ML model
│   ├── requirements.txt    # Python dependencies
│   ├── .gitignore         # Backend ignore rules
│   └── README.md          # Backend documentation
├── frontend/               # Next.js web application
│   ├── src/
│   │   └── app/           # App router pages
│   ├── public/            # Static assets
│   ├── package.json       # Node.js dependencies
│   ├── .env.example       # Environment template
│   └── README.md          # Frontend documentation
├── .gitignore             # Global ignore rules
└── README.md              # This file

🚀 Quick Start

1. Clone the Repository

git clone https://github.com/Tejeswar001/Placement-Prediction-App.git
cd APP

2. Setup Backend

cd backend

# Create virtual environment
python -m venv .venv

# Activate virtual environment
# On Linux/Mac:
source .venv/bin/activate
# On Windows:
# .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the backend server
cd src
python app.py

The backend API will be available at http://localhost:5000

3. Setup Frontend

Open a new terminal window:

cd frontend

# Install dependencies
npm install

# Create environment file
cp .env.example .env.local

# Start development server
npm run dev

The frontend will be available at http://localhost:3000

🔧 Configuration

Backend Configuration

The backend runs on Flask's default settings. For production:

1. Set environment variables:

   export FLASK_ENV=production
   export FLASK_DEBUG=False

2. Use a production WSGI server:

   gunicorn -w 4 -b 0.0.0.0:5000 app:app

Frontend Configuration

Update .env.local with your backend URL:

NEXT_PUBLIC_API_URL=http://localhost:5000

For production deployment, update the API URL to your production backend.

📊 Model Information

The application uses a Random Forest model trained on student data with the following features:

• IQ Student’s IQ score (normally distributed around 100)
• Prev_Sem_Result GPA from the previous semester (range: 5.0 to 10.0)
• CGPA Cumulative Grade Point Average (range: ~5.0 to 10.0)
• Academic_Performance Annual academic rating (scale: 1 to 10)
• Internship_Experience Whether the student has completed any internship (Yes/No)
• Extra_Curricular_Score Involvement in extracurriculars (score from 0 to 10)
• Communication_Skills Soft skill rating (scale: 1 to 10)
• Projects_Completed Number of academic/technical projects completed (0 to 5)

Output: Binary classification (0 = Not Placed, 1 = Placed)

🌐 API Endpoints

POST /predict

Predicts placement probability for a student.

Request:

{
  "features": ['IQ', 'Prev_Sem_Result', 'CGPA', 'Academic_Performance',
       'Internship_Experience', 'Extra_Curricular_Score',
       'Communication_Skills', 'Projects_Completed']
}

Response:

{
  "prediction": 0 or 1
}

📱 Usage

1. Navigate to the frontend application at http://localhost:3000
2. Go to the prediction page
3. Enter student details:
   • CGPA (0.0 - 10.0)
   • IQ Score (80 - 200)
   • Profile Score (0 - 100)
   • Experience (years)
4. Click "Predict Placement" to get the result
5. View the prediction result

🛠️ Development

Backend Development

cd backend
source .venv/bin/activate  # Activate virtual environment
cd src
python app.py  # Run with auto-reload enabled

Frontend Development

cd frontend
npm run dev  # Run with hot reload

Code Quality

Backend:

# Install development dependencies
pip install flake8 black pytest

# Format code
black .

# Lint code
flake8 .

# Run tests
pytest

Frontend:

# Lint code
npm run lint

# Fix linting issues
npm run lint -- --fix

🚀 Production Deployment

Backend Deployment

1. Environment Setup:

   pip install gunicorn

2. Run with Gunicorn:

   gunicorn -w 4 -b 0.0.0.0:5000 app:app

3. Docker Deployment (optional):

   FROM python:3.9-slim
   WORKDIR /app
   COPY requirements.txt .
   RUN pip install -r requirements.txt
   COPY src/ .
   EXPOSE 5000
   CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:5000", "app:app"]

Frontend Deployment

1. Build for Production:

   npm run build

2. Start Production Server:

   npm start

3. Deploy to Vercel:

   npm install -g vercel
   vercel --prod

🧪 Testing

Backend Testing

cd backend
python -m pytest tests/

Frontend Testing

cd frontend
npm test

📈 Performance

• Backend: Flask with Gunicorn can handle ~1000 requests/second
• Frontend: Next.js optimized builds with automatic code splitting
• Model: Random Forest provides fast inference (~1ms per prediction)

🔒 Security

• CORS: Properly configured for cross-origin requests
• Input Validation: All inputs are validated on both client and server
• Environment Variables: Sensitive data stored in environment files
• Production Settings: Debug mode disabled in production

🤝 Contributing

1. Fork the repository
2. Create a feature branch:

   git checkout -b feature/amazing-feature

3. Commit your changes:

   git commit -m 'Add amazing feature'

4. Push to the branch:

   git push origin feature/amazing-feature

5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Authors

• Your Name - Tejeswar Naidu - GitHub

🙏 Acknowledgments

• Flask team for the excellent web framework
• Next.js team for the amazing React framework
• scikit-learn contributors for the machine learning tools

📞 Support

If you have any questions or need help:

1. Check the documentation in each directory
2. Open an issue on GitHub

---

Happy Coding! 🎉