{"id":25583597,"url":"https://github.com/frauvate/cheatsheet","last_synced_at":"2026-04-11T19:33:48.608Z","repository":{"id":247740294,"uuid":"826721776","full_name":"frauvate/cheatsheet","owner":"frauvate","description":"This project provides ready-to-use templates for machine learning base models. 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This includes data cleaning, normalization, and feature engineering techniques.\n- **Simple Linear Regression**: A Jupyter notebook template for implementing and understanding\n  simple linear regression models.\n- **Multiple Linear Regression**: A Jupyter notebook template for implementing and understanding\n  multiple linear regression models.\n\n## Usage\n1. **Clone the Repository**: \n   ```bash\n   git clone https://github.com/yourusername/cheatsheet.git\n2. **Navigate to the Desired Notebook**:\n    Open the notebook in your preferred environment (e.g., Jupyter Notebook, Google Colab).\n    Customize the templates according to your data and requirements.\n3. **Run the Code**:\nEach notebook contains code cells that can be executed sequentially.\nModify the code as needed for your specific use case.\n\n## Requirements\nTo run the notebooks, you will need the following Python libraries:\n\n- numpy\n- pandas\n- scikit-learn\n- matplotlib\n- seaborn\n\nYou can install these libraries using pip:\n  ```bash\n  pip install numpy pandas scikit-learn matplotlib seaborn\n  ```\n## Contrubuting\nContributions are welcome! If you have templates for other models or improvements to existing\nones, feel free to open a pull request or submit an issue.\n\n## License\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n---\nMade with ❤️ by Esma\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffrauvate%2Fcheatsheet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffrauvate%2Fcheatsheet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffrauvate%2Fcheatsheet/lists"}