Student_Performance_Predictor

This repository is a comprehensive machine learning project designed to demonstrate a complete ML pipeline, from data processing and model training to deployment via a web application. The project structure and scripts enable modular development and easy extension.

Features

• End-to-End Machine Learning Pipeline:
  Modular code for data ingestion, cleaning, feature engineering, model training, evaluation, and prediction.

• Web Application Deployment:
  A Flask-based web app (application.py) allows users to interact with the trained model and make predictions via a browser interface.

• Exception Handling and Logging:
  Custom modules for robust error handling (src/exception.py) and detailed logging (src/logger.py) to ensure maintainability and traceability.

• Reusable Utilities:
  Utility functions in src/utils.py for common tasks such as data transformation, file I/O, and model persistence.

• Component-based Design:
  The src/components directory is intended for modular pipeline components (e.g., data loader, transformer, trainer).

• Pipeline Management:
  The src/pipeline directory contains scripts for orchestrating the ML workflow stages.

• Artifacts and Model Outputs:
  Trained models, preprocessing objects, and other outputs are stored in the artifacts directory for reproducibility and easy deployment.

• Experiment Tracking:
  The catboost_info directory is used to log CatBoost model training details.

• Interactive Notebooks:
  The notebook directory contains Jupyter notebooks for data analysis, prototyping, and exploratory data analysis (EDA).

• Custom Templates:
  The templates directory contains HTML files for the web application's frontend.

Project Structure

├── .ebextensions/         # Deployment configuration for AWS Elastic Beanstalk
├── artifacts/             # Model artifacts, checkpoints, and outputs
├── catboost_info/         # CatBoost training logs and info
├── notebook/              # Jupyter notebooks for EDA and prototyping
├── src/
│   ├── __init__.py
│   ├── components/        # Modular pipeline components
│   ├── exception.py       # Custom exception handling
│   ├── logger.py          # Logging setup and utilities
│   ├── pipeline/          # Pipeline orchestration scripts
│   └── utils.py           # Utility functions
├── templates/             # HTML templates for the web interface
├── application.py         # Flask app entry point
├── requirements.txt       # Python dependencies
├── setup.py               # Package installation script
├── .gitignore             # Git ignore rules
└── README.md              # Project documentation

Getting Started

1. Clone the repository

   git clone https://github.com/imswatisinha/ML_Project.git
   cd ML_Project

2. Install dependencies

   pip install -r requirements.txt

3. Run the web application

   python application.py

   Then, open your browser and go to http://localhost:5000.

4. Explore Notebooks

   • Navigate to the notebook directory and open .ipynb files using Jupyter Notebook or JupyterLab.

Customization

• To extend or modify pipeline components, add or edit scripts under src/components or src/pipeline.
• Update HTML templates in the templates directory to customize the web UI.
• Enhance utilities and logging as needed for your workflow.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License.

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For any questions, open an issue in the repository or contact the maintainer.