Support Vector Machine (SVM) is one of the machine learning’s classifier. Its goal is to find the optimal separating hyperplane which maximizes the margin of the data train. there are three important parts of this algorithm,namely the optimal separating hyperplane, the margin, and the data train. SVM will be implemented in a data train, so it is a supervised learning algorithm. this algorithm classifies the data into a certain class which makes it as a classification algorithm. To predict a class of a new data, SVM uses a hyperplane as the model that separates the classes and we can classify a new data just by looking at its position towards the hyperplane.

**What Is Hyperplane?**we know that the data train can be implemented in a space having any dimensions. If we use a two-dimensional space, the hyperplane becomes a line. If we use a three-dimensional space, it becomes a plane. And in more dimensions, it becomes a hyperplane. So, a hyperplane is just a generalization for a plane which th…