🎓 Student Performance Prediction System

An end-to-end Machine Learning + Full Stack project that predicts a student's academic performance using semester-long data such as attendance, study hours, quiz scores, assignments, and engagement.

This system provides:

• 📊 Performance prediction (Grade A–F)
• ⚠️ Risk level detection (Low / Medium / High)
• 💡 Personalized intervention suggestions
• 🌐 FastAPI backend for real-time prediction
• 🎨 Premium Next.js dashboard for visualization

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📌 Objective

To build an intelligent system that helps educational institutions:

• Identify weak students early
• Improve academic performance
• Enable personalized learning
• Prevent dropouts using data-driven insights

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🧠 How the System Works

Student Input Data
        ↓
Data Preprocessing
        ↓
Feature Engineering
        ↓
Machine Learning Model (XGBoost)
        ↓
FastAPI Backend (Prediction API)
        ↓
Next.js Dashboard (UI)
        ↓
Grade + Risk + Intervention Output

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🧪 Dataset & Simulation

Since real academic data is sensitive, this project uses synthetic data generation.

🔹 How Simulation Works:

• Random but realistic student data is generated

• Academic patterns are simulated:

  • High attendance → better scores
  • Low study hours → lower performance

• Target variable (grade_band) is assigned based on performance rules

Features Used:

• Gender
• Attendance (%)
• Study hours per week
• Quiz average
• Assignment score
• Midterm score
• LMS logins
• Forum posts
• Previous GPA
• Sleep hours
• Internet access
• Parent education

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🤖 Machine Learning Model

Model Used:

XGBoost Classifier

Why XGBoost?

• Works well on structured/tabular data
• Handles non-linear relationships
• High accuracy and performance
• Industry-level model

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📊 Model Output

Example API Response:

{
  "predicted_grade": "A",
  "confidence": 74.49,
  "risk_level": "Low Risk",
  "alert": "Student is on track.",
  "intervention": "Maintain current learning plan."
}

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📂 Folder Structure

Student-Performance-Prediction-System/
│
├── app/                     # FastAPI backend
│   └── main.py
│
├── data/
│   ├── raw/                 # Generated dataset
│   └── processed/
│
├── dashboard/              # Next.js frontend
│   ├── app/
│   ├── globals.css
│   └── package.json
│
├── models/                 # Saved ML models
│   ├── model.pkl
│   └── encoder.pkl
│
├── src/                    # ML pipeline
│   ├── generate_data.py
│   ├── train.py
│   ├── predict.py
│   ├── preprocess.py
│   └── evaluate.py
│
├── outputs/                # Reports
│
├── images/                 # Screenshots
│
├── requirements.txt
├── README.md
└── main.py

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⚙️ Setup & Installation

1️⃣ Clone Repository

git clone https://github.com/VaidehiDeore/Student-Performance-Prediction-System.git
cd Student-Performance-Prediction-System

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2️⃣ Create Virtual Environment

python -m venv venv

Activate:

venv\Scripts\activate

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3️⃣ Install Dependencies

pip install -r requirements.txt

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⚠️ Fix Memory Issue (Windows)

$env:OPENBLAS_NUM_THREADS="1"
$env:OMP_NUM_THREADS="1"
$env:MKL_NUM_THREADS="1"
$env:NUMEXPR_NUM_THREADS="1"

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🚀 Run Full Pipeline

Step 1 — Generate Dataset

python src/generate_data.py

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Step 2 — Train Model

python src/train.py

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Step 3 — Test Prediction

python src/predict.py

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🌐 Run Backend (FastAPI)

uvicorn app.main:app --reload

Open:

http://127.0.0.1:8000/docs

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🎨 Run Frontend (Dashboard)

cd dashboard
npm install
npm run dev

Open:

http://localhost:3000

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🖥️ Dashboard Features

• 📊 Performance bar graph
• 🎯 Circular academic metrics
• 📥 Dynamic input form
• 📈 Prediction result panel
• ⚠️ Risk level visualization
• 💡 Intervention suggestions

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📸 Screenshots

Full Dashboard

Input Form

Performance Graph

Prediction Result

API Docs

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📊 Key Insights

• Attendance strongly impacts performance
• Study hours correlate with higher grades
• LMS engagement improves outcomes
• Low engagement → higher risk

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🧠 Key Learnings

• Building end-to-end ML systems
• Data simulation techniques
• Feature engineering
• XGBoost model training
• FastAPI backend development
• Next.js frontend integration
• Dashboard UI design
• GitHub project structuring

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🔮 Future Improvements

• CSV upload for batch predictions
• SHAP model explainability
• User login system
• Database integration
• Deployment (AWS / Vercel)
• Model monitoring

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👩‍💻 Author

Vaidehi Deore Second Year Engineering Student

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⭐ Project Status

Completed End-to-End:
Dataset → ML Model → FastAPI → Next.js Dashboard → Prediction Output