Principal Component Analysis (PCA) is a popular unsupervised learning technique used for dimensionality reduction and feature extraction. PCA transforms a high-dimensional dataset into a lower-dimensional space while retaining the maximum amount of variance in the data. Principal Component Analysis (PCA) works by finding a set of orthogonal vectors, called principal components.