Principal Component Analysis (PCA) is a technique that reduces dimensionality by creating new uncorrelated variables called _______. These new variables retain most of the variability in the original dataset.

Principal Component Analysis (PCA) is a technique that reduces dimensionality by creating new uncorrelated variables called principal components. These new variables retain most of the variability in the original dataset. PCA works by projecting the original data onto a new space, represented by the principal components, which are orthogonal to each other and thus uncorrelated.