🎓 Student Marks Prediction (Machine Learning Project)

A simple and beginner-friendly Machine Learning Regression Project that predicts student marks based on:

• Hours studied
• Attendance percentage ..
• Assignments submitted

This project is built completely using Python (.py files only) with a clean, industry-level folder structure.

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🚀 Features

• Clean and modular ML pipeline using .py scripts
• Linear Regression model for prediction
• Streamlit Web App for interactive UI
• Fully reproducible project with dataset included
• Accurate model training with saved .pkl file
• Beginner-friendly and deployment ready

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🧠 Machine Learning Model Used

Linear Regression

• Supervised Learning
• Regression algorithm
• Best for predicting numeric values
• Provides clear relationship between study habits and marks

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📁 Project Structure

student-marks-prediction/ │ ├── data/ │ └── student_marks.csv │ ├── src/ │ ├── train_model.py │ ├── predict.py │ └── utils.py │ ├── app/ │ └── streamlit_app.py │ ├── models/ │ └── linear_model.pkl │ ├── README.md └── requirements.txt

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🛠️ How to Run the Project

1. Install dependencies

pip install -r requirements.txt

2. Train the model

python src/train_model.py

3. Run Streamlit app

🏁 Conclusion

This project demonstrates a complete ML workflow, including:

• Data processing
• Model training
• Saving/loading models
• Building UI
• Deployment

Perfect for beginners starting in AI/ML