Support Vector Machine or SVM is one of the most popular Supervised Machine Learning algorithms, which is used for Regression and Classification problems in ML. However, primarily, it is used for Classification problems in Machine Learning.
It was developed by Vladimir Vapnik at AT&T Bell Laboratories.
The goal of the SVM algorithm is to create the best line or decision boundary that can distinctly classifies n-dimensional space datasets into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary or this single line is called hyperplane.
SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine.
We implemented the approach as discussed using Support Vector Machine(SVM)- linear and non-linear and on different data sets- AND, XOR.
To use the files, install RStudio and R Language.