In this repository, a Support Vector Machine (SVM) is applied to the Pulsar Star Classification dataset available on Kaggle.
SVM is a supervised machine learning algorithm used for classification and regression problems. It was proposed by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1963.
A hyperplane is a crucial part of SVM; it is a decision boundary that separates a set of data points. SVM separates data points using a hyperplane with the maximum margin. The margin is the separation gap between two lines closest to the data points from each class.
SVM is implemented using a kernel. The role of the kernel is to map data to a higher dimension where the data becomes separable.