How Principal Component Analysis [PCA] Works

What is PCA: Principal Component Analysis (PCA) is a dimensionality reduction technique that enables you to identify correlations and patterns in a data set so that it can be transferred into a data set of significantly lower dimensions without loss of any important information. Step 1: Standardization of the data Standardization is all about scaling your data in such a way that all the variables and values lie within a similar range. Step 2: Computing the covariance matrix A covariance matrix expresses the correlation between the different variables in the data set. It is essential to identify heavily dependent variables because they contain biased and redundant information which reduces the overall performance of the model. Eigenvectors and Eigenvalues are the mathematical constructs that must be computed from the covariance matrix in order to determine the Principal component of the data sets.