🎓 StudentRetain - Student Dropout Risk Prediction System

An ML-powered student dropout risk prediction system with personalized support recommendations.

Design Thinking Lab Project - RV College of Engineering

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👥 Team

• Sathvik K Y - 1RV24CS255
• Sandesh S Patrot - 1RV24CS250
• Roshan George - 1RV24CS235
• S Dheeran - 1RV24CS237

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🚀 Features

• ✅ ML-Based Prediction: Random Forest model with 85% accuracy, 0.89 AUC-ROC
• ✅ Privacy-First: Explicit consent collection with GDPR-compliant approach
• ✅ Multi-Factor Assessment: Academic, mental health, financial indicators
• ✅ Personalized Recommendations: Tailored support based on risk factors
• ✅ Admin Dashboard: Real-time analytics and trend visualization
• ✅ Responsive Design: Modern UI with Tailwind CSS and Framer Motion

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📁 Project Structure

Student_Dropout_Risk/
├── frontend/                 # Next.js 14 Application
│   ├── app/                  # Pages (Home, Assessment, Results, Dashboard, About)
│   ├── components/           # React components
│   ├── lib/                  # API client & utilities
│   ├── types/                # TypeScript types
│   └── package.json
│
└── backend/                  # FastAPI Application
    ├── app/
    │   ├── models/           # Pydantic schemas & ML model
    │   ├── routers/          # API endpoints
    │   └── main.py           # FastAPI entry point
    ├── ml/
    │   ├── train_model.py    # Model training script
    │   ├── data/             # Dataset (download separately)
    │   └── saved_models/     # Trained models
    └── requirements.txt

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🛠️ Tech Stack

Frontend

• Next.js 14 with App Router
• React 18 with TypeScript
• Tailwind CSS + shadcn/ui
• Framer Motion (animations)
• Recharts (data visualization)

Backend & ML

• FastAPI (Python web framework)
• scikit-learn (Random Forest model)
• Grid Search optimization
• 5-fold cross-validation
• Kaggle Dataset (4,424 students)

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🔧 Setup Instructions

Prerequisites

• Node.js 18+ and npm
• Python 3.9+
• Git

1. Frontend Setup

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

Frontend will be available at: http://localhost:3000

2. Backend Setup

# Navigate to backend directory
cd backend

# Create virtual environment
python -m venv venv

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

# Install dependencies
pip install -r requirements.txt

# Start backend server
uvicorn app.main:app --reload

Backend API will be available at: http://localhost:8000 API Docs: http://localhost:8000/docs

3. Dataset & Model Training (Optional)

Download the dataset from Kaggle:

• URL: https://www.kaggle.com/datasets/thedevastator/higher-education-predictors-of-student-retention
• Place dataset.csv in backend/ml/data/

Train the model:

cd backend/ml
python train_model.py

This will generate:

• saved_models/model.joblib (trained model)
• saved_models/scaler.joblib (feature scaler)

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📖 Usage

1. Home Page: Overview of the system with key statistics
2. Take Assessment: 5-step form with consent, academic, support, personal, and services questions
3. View Results: Risk score, identified factors, and personalized recommendations
4. Dashboard: Admin view with analytics, trends, and recent assessments
5. About: Project information, team details, and future scope

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🌐 Deployment

Frontend (Vercel)

cd frontend
npm run build
# Deploy to Vercel
vercel deploy

Backend (Render / Railway)

Create Dockerfile in backend:

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

Deploy to Render or Railway following their deployment guides.

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📊 Model Performance

• Accuracy: 85%
• AUC-ROC: 0.89
• Training Data: 4,424 students
• Features: 8 optimal features (F6 set)
• Algorithm: Random Forest with Grid Search

Top 3 Predictive Features:

1. Curricular units 2nd semester (approved)
2. Curricular units 1st semester (approved)
3. Tuition fees up to date

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🔮 Future Scope

• 🔮 College ERP Integration: Connect with institutional databases
• 🔮 Multi-Stakeholder Views: Dashboards for Faculty, Counsellors, Admin
• 🔮 Automated Alerts: Email/SMS notifications for high-risk students
• 🔮 Periodic Reassessment: Track student progress over time
• 🔮 Continuous Model Improvement: Fine-tune with real institutional data
• 🔮 Mobile Application: Native apps for iOS and Android

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📝 License

This project is part of a Design Thinking Lab course at RV College of Engineering.

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📞 Contact

For questions or feedback, contact the team at RV College of Engineering.

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Built with ❤️ using Next.js, FastAPI, and Random Forest ML