Support Vector Machine Implementation

Support Vector Machines (SVM) represent a widely-used machine learning algorithm that can accomplish numerous complex tasks when implemented using MATLAB. This robust yet accessible methodology finds applications across diverse domains. In this project, we employ the SVM algorithm to address a specific problem, with implementation details including MATLAB's built-in functions like fitcsvm for classification tasks and fitrsvm for regression problems. The solution incorporates key SVM components such as kernel functions (linear, polynomial, RBF), hyperparameter optimization using cross-validation, and margin maximization techniques. The complete analysis and results are presented in Word document format, facilitating comprehensive understanding and detailed examination. This documentation includes code snippets demonstrating data preprocessing, model training with svmtrain or fitcsvm functions, and prediction implementation using predict. The thorough implementation approach aims to provide valuable insights for practical SVM applications.