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🎓 Student Performance Analyzer

A Machine Learning dashboard that predicts student performance using Linear Regression implemented from scratch using Streamlit.

🚀 Project Overview

This project analyzes student academic data and predicts the Performance Index based on:

  • Hours Studied
  • Previous Scores
  • Sleep Hours
  • Sample Question Papers Practiced
  • Extracurricular Activities

The goal of this project was to understand how Linear Regression works mathematically by implementing it manually using NumPy instead of relying on ready-made ML libraries like sklearn.

🧠 Machine Learning Approach

  • Implemented Linear Regression using the Normal Equation
  • Used matrix multiplication to calculate model coefficients (theta values)
  • Evaluated model performance using R² Score
  • Built a custom grading classification system

📊 Features

✔ Student Performance Prediction
✔ Custom Grade Classification
✔ Model Accuracy Display (R² Score)
✔ Interactive Streamlit Dashboard
✔ Animated & Colorful Visualizations
✔ Clean Project Structure (model + UI separation)

🛠 Technologies Used

  • Python
  • NumPy
  • Pandas
  • Matplotlib
  • Plotly
  • Streamlit

📁 Project Structure

student-performance-analyzer/
│
├── app.py                 # Streamlit dashboard
├── model.py               # Linear Regression model logic
├── data/
│   ├── readme.md                  # Dataset description
│   └──dataset.csv                 # Student Performance Dataset
│
├── notebooks/
│     │
│     └── Student_Performance_Analysis.ipynb
│
│
├── requirements.txt
└── README.md

🎯 Learning Outcome

This project helped me:

  • Understand Linear Regression mathematically
  • Work with NumPy matrix operations
  • Convert data analysis notebook into a web application
  • Debug and structure a real ML project
  • Deploy a working dashboard

📌 Future Improvements

  • Add Train/Test split
  • Add more evaluation metrics (MAE, MSE)
  • Deploy the project online
  • Improve UI/UX design

👨‍💻 Author

Raj Kumar | B.Tech Student | Aspiring AI & ML Student

⭐ If you found this project interesting, feel free to star the repository!