Support Vector Machine Classification - Breast Cancer Dataset

This code implementation offers detailed explanations, though supplementing it with comprehensive documentation would further enhance understanding of its working principles and usage methodologies. We could incorporate practical examples demonstrating various applications of the code and its performance across different scenarios. Additionally, implementing test cases would ensure code correctness and reliability while facilitating verification by other developers. The SVM algorithm implementation includes key components like kernel function selection (linear/RBF), hyperparameter optimization, and cross-validation techniques. By enhancing documentation with code structure explanations, adding usage examples with different dataset configurations, and integrating unit tests for classification accuracy validation, we can significantly improve the code's robustness and accessibility, thereby increasing its value for machine learning projects.