Intellectus — Student Risk Analysis System! 📊🎓

A hackathon-ready prototype for early student risk detection that combines spreadsheet data, rule-based analytics, and explainable machine learning to help institutions identify at-risk students and enable timely interventions.
Frontend is deployed on GitHub Pages; the FastAPI + ML backend runs locally. :contentReference[oaicite:1]{index=1}

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🌐 UI Preview (GitHub Pages)

🔗 https://anuz-bit.github.io/Intellectus/

Heads-up: This is a frontend-only preview.
The FastAPI + ML backend runs locally (see Run Locally section).

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✨ Features

• Upload multiple student datasets
• Machine Learning-based risk prediction
• Fast, scalable backend using FastAPI
• Modern, responsive UI
• Simple and clean interface
• Easy to run locally :contentReference[oaicite:4]{index=4}

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🧱 Tech Stack

Frontend

• React (Vite)
• Tailwind CSS
• JavaScript :contentReference[oaicite:5]{index=5}

Backend

• Python
• FastAPI :contentReference[oaicite:6]{index=6}

Machine Learning

• Pandas, NumPy
• scikit-learn
• Joblib :contentReference[oaicite:7]{index=7}

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📁 Project Structure

Intellectus/
├── asset/                         # Screenshots used in README
├── backend/
│   ├── main.py                    # FastAPI backend & ML logic
│   ├── train_model.py             # Model training script
│   ├── student_risk_model.joblib  # Trained ML model
│   ├── feature_names.joblib
│   ├── fee_status_encoder.joblib
│   └── venv/                      # Local virtual environment (do not commit)
└── frontend/
    ├── src/                       # React source code
    ├── public/
    └── package.json

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✅ Prerequisites

• Python 3.9+
• Node.js 18+
• npm

Verify installations:

python --version
node --version
npm --version

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🚀 Run Locally (Step-by-Step)

1) Clone the repository

git clone https://github.com/Anuz-bit/Intellectus.git
cd Intellectus

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2) Backend (FastAPI + ML)

cd backend
python -m venv venv

Activate the virtual environment:

Windows

venv\Scripts\activate

macOS/Linux

source venv/bin/activate

Install dependencies:

python -m pip install -r requirements.txt

If requirements.txt is missing, install manually:

python -m pip install pandas numpy scikit-learn joblib fastapi uvicorn python-multipart openpyxl

Start the backend server:

uvicorn main:app --reload

Backend runs at:

• http://127.0.0.1:8000

API docs (if enabled):

• http://127.0.0.1:8000/docs

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3) Frontend (React + Vite)

Open a new terminal:

cd frontend
npm install
npm run dev

Frontend runs at:

• http://localhost:5173

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🧪 Usage

1. Open the app: http://localhost:5173

2. Upload the datasets:

   • students.xlsx
   • academic_records.csv
   • activity_records.csv

3. Click Analyze Data

4. Review results in:

   • Risk distribution dashboard
   • Student risk overview table
   • Individual student detail view (GitHub)

Tip: If you provide sample datasets in the repo later, link them here (e.g., data/ folder) to make onboarding faster.

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🖼️ Screenshots

Home / Upload	Dashboard

Risk Table	Student Detail

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🛣️ Roadmap

Planned improvements:

• Interactive dashboards
• Authentication (Admin / Teacher)
• Database integration
• Automated alerts
• Cloud deployment (GitHub)

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🤝 Contributing

Contributions are welcome!

1. Fork the repo

2. Create a feature branch:

   git checkout -b feat/your-feature

3. Commit your changes:

   git commit -m "Add: your feature"

4. Push the branch:

   git push origin feat/your-feature

5. Open a Pull Request

Development guidelines

• Keep PRs focused and small
• Update docs when behavior changes
• Prefer readable code + clear naming
• Add minimal validation for uploaded files (schema/columns) where applicable

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👨‍💻 Author

Anuj Wankhede B.Tech Student | Machine Learning & Data Analytics Enthusiast