Here’s a sample README for your "StudentDropOutDector" project on GitHub:
This project aims to build an AI model to predict student success in a class or a degree program. By analyzing historical data, the model identifies students at risk of dropping out, enabling proactive intervention to improve retention rates.
- Predicts student dropout likelihood using machine learning algorithms.
- Utilizes models like XGBoost and Random Forest for comparison.
- Designed for education institutions to enhance student support.
- Python 3.7+
- scikit-learn
- XGBoost
- pandas, numpy
- Clone the repository:
git clone https://github.com/4Gedu/StudentDropOutDector.git - Install dependencies:
pip install -r
- Prepare your dataset in CSV format.
- Data incoming from Mockaroo
- Run the model script:
python DropOutModelAi.py
This project is licensed under the MIT License - see the LICENSE file for details.
This README covers basic setup, usage, and details of the project to make it easy for contributors or users to understand.