An end-to-end MERN + Machine Learning project where teachers can manage student records and predict their performance (Pass/Fail) based on inputs like attendance, study hours, previous marks, etc.
- Node.js + Express
- MongoDB (Mongoose)
- JWT Authentication
- Python
- Pandas, Scikit-learn
- Joblib
- React.js
- Axios
- Tailwind
- Material UI
- React Router DOM, Formik + Yup, Toastify
- ✅ Teacher Register/Login
- ✅ Add Students with data
- ✅ Predict student result via ML
- ✅ Store prediction history
- ✅ Update student data
- ✅ View single student & their predictions
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Trained with Logistic Regression on:
- Attendance (%)
- Study Hours per Day
- Previous Marks (%)
- Assignment Score
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Output:
PassorFail -
Microservice built in Python, integrated via
child_process.spawnin Node.js
📦 root ├── 📁 server # Backend + ML Microservice │ ├── 📁 microservice # Python ML code │ │ ├── generate_training_data_1000.py │ │ ├── predict.py │ │ ├── student_performance_dataset.csv │ │ ├── student_performance_model.joblib │ │ └── train_model.py │ ├── 📁 src # Node.js + Express backend │ │ ├── 📁 controllers │ │ ├── 📁 middlewares │ │ ├── 📁 models │ │ └── 📁 routes │ ├── .env.example │ ├── .gitignore │ ├── package-lock.json │ ├── package.json │ └── server.js ├── 📁 client # React.js Frontend │ ├── 📁 public │ ├── 📁 src │ │ ├── 📁 assets │ │ ├── 📁 pages │ │ │ ├── 📁 Auth │ │ │ │ ├── LoginPage.jsx │ │ │ │ └── RegisterPage.jsx │ │ │ ├── AddStudentForm.jsx │ │ │ ├── EditStudentForm.jsx │ │ │ ├── Dashboard.jsx │ │ │ ├── Header.jsx │ │ │ └── StudentDetails.jsx │ │ ├── 📁 services │ │ │ ├── api.js │ │ │ └── toastNotifications.js │ │ ├── App.css │ │ ├── App.jsx │ │ ├── constants.js │ │ ├── index.css │ │ ├── main.jsx │ │ └── routes.jsx │ ├── .env │ ├── .gitignore │ ├── README.md │ └── eslint.config.js ├── README.md