Support Vector Machine (SVM) is a supervised learning model and algorithm used for classification and regression tasks​An SVM works by finding an optimal hyperplane that separates data points of different classes with the maximum margin between them. In the linearly separable case, the algorithm identifies the hyperplane that maximizes the distance to the nearest training points of any class (these nearest points are the “support vectors”). This maximum-margin hyperplane leads to better generalization, as a larger margin usually lowers overfitting​.For data that are not linearly separable in the original feature space, SVMs employ the kernel trick: the data is implicitly mapped to a higher-dimensional space where a linear separator might exist, by using a kernel function (e.g., Gaussian/RBF, polynomial) to compute similarities​.SVMs are known for their robustness in high-dimensional spaces and have been successfully used in applications like text classification and image recognition. They aim to minimize classification errors while maximizing the margin, often leading to a convex optimization problem that has a unique, globally optimal solution.