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It provides a practical implementation of this regression technique using Python libraries, visualizations, and real-world manufacturing data.\n\n## Features\n\n- Polynomial regression modeling for non-linear data.\n\n- Data preprocessing and exploratory analysis.\n\n- Model evaluation metrics for performance comparison.\n\n- Visualization of regression curves and predictions.\n\n\n## Requirements\n\n- Python 3.x\n\n- Libraries: numpy, pandas, matplotlib, sklearn\n\n\n## Usage\n\n1. Clone the repository:\n```\ngit clone \u003crepository-url\u003e\n```\n\n2. Install required dependencies:\n```\npip install -r requirements.txt\n```\n\n3. Open the Jupyter Notebook:\n```\njupyter notebook Polynomial_Regression_Manufacturing.ipynb\n```\n\n4. Follow the step-by-step implementation within the notebook.\n\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE). See the LICENSE file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnurulashraf%2Fpolynomial-regression-manufacturing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnurulashraf%2Fpolynomial-regression-manufacturing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnurulashraf%2Fpolynomial-regression-manufacturing/lists"}