Student Math Score Prediction Project

A machine learning project focused on predicting students’ math performance scores using demographic, academic, and socio-economic features. The project includes the full workflow — from data exploration and preprocessing to model training, evaluation, and deployment through a simple web application.

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🚀 Project Overview

This project predicts student math scores based on features such as study habits, parental education levels, test preparation, and other academic indicators. It demonstrates:

• Feature engineering for educational datasets
• Model training and hyperparameter tuning
• Evaluation using regression metrics
• Saving trained models and transformers
• Deployment through a Flask web application

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✅ Features

• Modular ML codebase inside src/
• EDA and model experimentation inside notebook/
• Automatic storage of model artifacts
• Web-based prediction interface
• Easy installation via setup.py
• Reproducible environment with requirements.txt

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📂 Project Structure

mlproject/
├── artifacts/              # Stores trained models, logs, and outputs
├── catboost_info/          # CatBoost training metadata
├── notebook/               # Jupyter notebooks for exploration and experiments
├── src/                    # ML pipeline modules
│   ├── data/               # Data loading & preprocessing
│   ├── features/           # Feature engineering
│   ├── modeling/           # Training, evaluation, model building
│   ├── utils/              # Helper utilities
│   └── ...
├── templates/              # HTML templates for the web app (if applicable)
├── application.py          # Main web application
├── setup.py                # Package configuration
├── requirements.txt        # Python dependencies
├── README.md               # Project documentation
└── .gitignore              # Ignored files

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🛠️ Getting Started

Follow these steps to set up and run the project.

Prerequisites

• Python 3.7+
• (Optional) Virtual environment

Installation

git clone https://github.com/ASHMEET555/mlproject.git
cd mlproject
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt

Run the Application

To start the web interface:

python application.py

Then visit the local URL shown in the terminal (e.g., http://127.0.0.1:5000).

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📘 Usage

• Use notebooks in notebook/ for EDA and training.
• Trained model artifacts are stored in artifacts/.
• application.py loads the model pipeline and exposes a UI/API.
• Extend the pipeline by modifying modules inside src/.

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🧠 Technologies Used

• Python
• CatBoost / Sklearn (as used in the notebooks and pipeline)
• Flask / Web framework for UI
• Jupyter Notebooks

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🤝 Contributing

Contributions are welcome! To contribute:

1. Fork the repository
2. Create a new branch (feature-xyz)
3. Commit your changes
4. Push and open a Pull Request

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📜 License

This project is licensed under the MIT License.

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📬 Contact

Author: Ashmeet GitHub: ASHMEET555

If you'd like badges, screenshots, or improvements to this README, feel free to ask!