Support Vector Machine (SVM) is a algorithm that every machine learning expert should have in their arsenal. It is a great algorithim as it yileds significant accuracy with less computational power. It can be used for both regression and classification tasks, though is widely used in classification objectives. The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space (N — the number of features) that distinctly classifies the data points. To separate the two classes of data points, there are many possible hyperplanes that could be chosen. The objective of the algorithim is to find a plane that has the maximum margin.
In this project we are going to use synthetic data provided in this repository. "X.csv” contains information pertaining to all the instances of the data set and “y.csv” has labels pertaining to each instance in “X.csv”. For each instance, there are two attributes and a total of 100 instances.