Support Vector Machine for Regression Analysis

In this documentation, I have developed a Support Vector Machine (SVM) regression analysis program. This implementation utilizes SVM algorithms for regression tasks, typically employing kernel functions (such as linear, polynomial, or RBF kernels) to map input features into higher-dimensional spaces for nonlinear pattern recognition. The program includes core functionalities for data preprocessing, model training with hyperparameter optimization (like C and epsilon parameters), and prediction evaluation. After downloading the program, you can design your own sample datasets and leverage the SVM regression capabilities to analyze complex relationships within your data. The implementation provides accurate regression results through efficient optimization techniques, such as sequential minimal optimization (SMO) for solving the quadratic programming problem inherent in SVM regression. Additionally, the program integrates multiple visualization tools to help users intuitively understand data distributions, regression curves, and trend patterns through plots like scatter diagrams with regression lines and residual analysis charts. By using this SVM regression program, you will enhance your ability to uncover data correlations, make precise predictions, and gain deeper insights for research and decision-making support.