What Is Principal Component Analysis? Principal Component Analysis (PCA) is a statistical technique used to simplify datasets containing many variables. It transforms the original variables into a smaller set of new variables called principal components while retaining as much of the data's variation as possible. PCA is widely used in statistics, machine learning, finance, and scientific research…