🎓 Student Performance Analyzer

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

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🚀 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.

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🧠 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

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📊 Features

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

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🛠 Technologies Used

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

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📁 Project Structure

student-performance-analyzer/
│
├── app.py                 # Streamlit dashboard
├── model.py               # Linear Regression model logic
├── StudentPerformance.csv # Dataset
├── requirements.txt
└── README.md

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🎯 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

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📌 Future Improvements

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

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👨‍💻 Author

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

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