The principal component analysis is a techique that allows us to reduce multi-dimensional dataset by using eigenvectors, eigenvalues
and singularity decomposition. It is possible to plot the data into two dimensions (x and y axis). The axis are
represented as the eigenvectors and are called Principal components (PC). The values along the axis are represented
as the eigenvalues and are also known as the variation.
By using PCA it will be easier to find relations between the dimensions.