What is Support Vector Machine (SVM) and how does it work?

SVM is a supervised learning algorithm that finds the optimal hyperplane separating classes with maximum margin.

Support Vector Machines aim to find a decision boundary (hyperplane) that maximizes the margin between classes. The closest points to the hyperplane are called support vectors, and they define the boundary. For non-linearly separable data, kernel functions (like RBF, polynomial) transform data into higher dimensions where separation becomes possible. SVM is highly effective in high-dimensional spaces and works well even with smaller datasets.