What is PCA? What does a PCA do?

PCA stands for Principal Component analysis. It is a dimensionality reduction technique that summarizes a large set of correlated variables (basically high dimensional data) into a smaller number of representative variables, called the Principal Components, that explains most of the variability of the original set i.e, not losing that much of the information.

PCA is a deterministic algorithm in which we have not any parameters to initialize and it doesn’t have a problem of local minima, like most of the machine learning algorithms has.