PCA = Principal Component Analysis

PCA is a statistical method used to reduce the dimensionality of data while retaining most of its variance. It transforms the original correlated variables into a set of uncorrelated variables called principal components. These components are ordered by the amount of variances they capture, making PCA an effective tool for simplifying complex datasets.