{"id":23291262,"url":"https://github.com/messi10tom/ai-based-grade-prediction","last_synced_at":"2026-05-02T03:31:36.824Z","repository":{"id":267668339,"uuid":"901973761","full_name":"messi10tom/AI-Based-Grade-Prediction","owner":"messi10tom","description":"GDSC task-1: Build a model to predict a student’s final grade based on features such as attendance, participation, assignment scores, and exam marks.","archived":false,"fork":false,"pushed_at":"2024-12-26T07:57:33.000Z","size":1188,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-06T17:52:11.323Z","etag":null,"topics":["ai","data-analysis","data-science","regression","streamlit"],"latest_commit_sha":null,"homepage":"https://grade-prediction.streamlit.app/","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AI-Based Grade Prediction\n\n![Project Banner](./doc/banner.webp)\n\n[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)\n[![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](https://www.python.org/downloads/release/python-380/)\n[![Streamlit](https://img.shields.io/badge/Streamlit-0.84.0-red.svg)](https://streamlit.io/)\n\n## Problem Statement\nBuild a model to predict a student’s final grade based on features such as attendance, participation, assignment scores, and exam marks.\n\n## Approach\n1. **Data Collection**: Utilized the Student Performance Dataset from the UCI Machine Learning Repository.\n2. **Data Preprocessing**: Cleaned and processed the data to handle categorical variables.\n3. **Model Training**: Trained an Artificial Neural Network(ANN) for regression.\n4. **Evaluation**: Evaluated models using RMSE and classification metrics like accuracy.\n5. **Feature Analysis**: Analyzed feature importance to identify key contributors to performance.\n\n\u003cimg src=\"./data/The_data_distribution.png\" alt=\"Distribution of Categorical Variables\" width=\"70%\"\u003e\n\n6. **Productization**: Developed a Streamlit web app for user interaction.\n\n## Results\n- **Model Performance**:\n\n    - ANN Model: RMSE = 0.2576\n\n## Challenges\n- **Feature Engineering**: Created new features to improve model performance.\n- **Model Overfitting**: Implemented regularization techniques to mitigate overfitting.\n\n## Installation\n1. Clone the repository:\n    ```bash\n        git clone https://github.com/messi10tom/AI-Based-Grade-Prediction.git\n        cd AI-Based-Grade-Prediction\n    ```\n2. Create and activate a virtual environment:\n    ```bash\n        python3 -m venv venv\n        source venv/bin/activate\n    ```\n3. Install the required packages:\n    ```bash\n        pip install -r requirements.txt\n    ```\n\n## Usage\n\n1. **Running the Web App**:\n    ```bash\n        streamlit run src/app.py\n    ```\n\n\n## Screenshots\n### Training logs\n![Training Logs](./train/train%20log.png)\n\n![Web App](./doc/streamlit%20UI.png)\n\n## License\nThis project is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). See the [LICENSE](LICENSE) file for details.\n\n---\n\nFor further details, visit the [dataset page on UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Student+Performance).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmessi10tom%2Fai-based-grade-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmessi10tom%2Fai-based-grade-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmessi10tom%2Fai-based-grade-prediction/lists"}