Student Performance Predictor 🎓

A simple Machine Learning project that predicts a student's final exam score based on:

• Hours Studied
• Attendance Percentage
• Previous Exam Score

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📌 Project Overview

This project uses Linear Regression to predict final scores using structured student performance data.

The workflow includes:

• Data loading
• Feature selection
• Train-test split
• Model training
• Model evaluation using Mean Squared Error

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🛠 Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn

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📂 Project Structure

student-performance-predictor/ │ ├── data/ │ └── student_data.csv ├── src/ │ └── main.py ├── requirements.txt ├── README.md └── LICENSE

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▶️ How to Run

1. Install dependencies:

2. Run the project:

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📊 Output

The model trains and prints the Mean Squared Error (MSE) to evaluate prediction accuracy.

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🚀 Future Improvements

• Add more data
• Try advanced models (Random Forest, XGBoost)
• Deploy as a web app using Flask or FastAPI