MATLAB Demonstration Program for Support Vector Machine (SVM) Regression

This MATLAB demonstration program focuses on Support Vector Machine (SVM) Regression, a powerful machine learning algorithm for solving regression problems. The program illustrates how to implement SVM regression in MATLAB, including key aspects such as selecting appropriate kernel functions (linear, polynomial, RBF) and tuning critical parameters like the box constraint and epsilon value. The implementation demonstrates SVM's core principle of finding the optimal hyperplane that maximizes the margin while minimizing regression errors. Through practical code examples, we show how to preprocess data, train SVM models using MATLAB's fitrsvm function, and evaluate performance metrics. The program includes real dataset applications to help users understand SVM regression's practical implementation, featuring data normalization techniques and result interpretation methods. By exploring this demonstration, users will gain deeper insights into SVM regression mechanics and learn to apply this algorithm effectively to real-world regression challenges. The code structure emphasizes reproducible workflows with clear comments explaining each computational step.