🔗 Live Demo: https://spa-analyzer.vercel.app
A full-stack web application that helps students analyze their academic performance across semesters using structured data, domain-based classification, and visual insights.
Student Performance Analyzer (SPA) is designed to transform raw academic marks into meaningful insights. Instead of just storing marks, the system intelligently groups subjects into domains and provides a clear picture of strengths, weaknesses, and trends.
This project is built with scalability in mind, enabling future integration of machine learning for predictive analytics and risk classification.
- User registration and login
- Secure authentication system
- Google OAuth integration (optional)
- Semester-wise dynamic input system
- Add unlimited semesters
- Subject-wise marks entry
- Dropdown + custom subject support
Subjects are automatically mapped into domains:
- Mathematics
- Programming
- Core Computer Science
- Electronics
- Science
- Mechanical
- Soft Skills
- General
Ensures:
- Consistent categorization
- Clean data for analytics
- ML-ready structure
- Domain-wise performance analysis
- Semester-wise comparison
- Strength & weakness identification
- Clean and intuitive UI
- Students ranked based on overall performance
- Encourages relative ranking and peer based learning
- Bar graphs (domain performance)
- Semester trends
- Comparative insights
- (Planned) Heatmaps
- Upload semester marksheet
- Auto-extract subjects & marks
- Autofill form for editing
- Risk prediction (low performance detection)
- Student clustering (peer learning groups)
- Domain-based future performance prediction
- React (Vite)
- Tailwind CSS
- Axios
- Node.js
- Express.js
- MongoDB (Mongoose)
- Frontend: Vercel
- Backend: Render
SPA/
├── frontend/ # React application
├── backend/ # Express server
├── README.md
VITE_API_URL=https://your-backend-url
VITE_GOOGLE_CLIENT_ID=your_google_client_id
PORT=5000
MONGO_URI=your_mongodb_connection
JWT_SECRET=your_secret_key
git clone https://github.com/your-username/spa.git
cd spa
cd frontend
npm install
cd ../backend
npm install
Frontend:
npm run dev
Backend:
npm start
- Full ML integration
- Advanced analytics dashboard
- Personalized recommendations
- Peer comparison system
- Resume insights based on performance
Most students only see marks — not patterns.
SPA was built to:
- Convert marks → insights
- Help students understand their strengths
- Enable data-driven academic growth
Lakshya Bhandari
This project is open-source and available under the MIT License.