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It includes several diagnostic measurements such as glucose concentration, blood pressure, skin thickness, insulin level, BMI, age, and more.\r\n\r\n## Key Features\r\n\r\n- **Data Preprocessing**: Includes handling missing values, feature scaling, and data transformations to prepare the dataset for modeling.\r\n- **Model Training and Evaluation**: Employs three different machine learning models:\r\n  - Logistic Regression\r\n  - K-Nearest Neighbors (KNN)\r\n  - Support Vector Machine (SVM)\r\n- **Performance Analysis**: Evaluates models based on accuracy, precision, and recall. Includes detailed visualizations of model performance.\r\n- **Data Visualization**: Uses Matplotlib and Seaborn for insightful visualizations of the dataset distribution and model outcomes.\r\n\r\n## Contributing\r\n\r\nContributions are welcome! For major changes, please open an issue first to discuss what you would like to change. Please ensure to update tests as appropriate.\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE.md) file for details.\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpialghosh2233%2Fdiabetes_prediction_using_ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpialghosh2233%2Fdiabetes_prediction_using_ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpialghosh2233%2Fdiabetes_prediction_using_ml/lists"}