Support Vector Machine Regression Algorithm Implementation

This program implements an efficient Support Vector Machine (SVM) regression algorithm, offering a robust and user-friendly tool to achieve enhanced performance on regression problems. The implementation incorporates advanced machine learning techniques and optimization algorithms, featuring key components such as kernel function selection (linear, polynomial, or RBF), loss function configuration (epsilon-insensitive or squared), and quadratic programming solvers for efficient handling of complex datasets. Users can perform comprehensive workflows including data preprocessing through standardization/normalization methods, automatic feature selection using recursive feature elimination, model training with cross-validation support, and prediction with confidence interval estimation. The code architecture employs object-oriented design with clear separation between data handlers, kernel calculators, and model trainers, ensuring maintainability and extensibility. This implementation aims to provide a complete and reliable solution that enables users to obtain superior results in practical applications through well-documented code structure and algorithmic explanations.