Principal Component Analysis (PCA) refers to a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional representation while preserving the most important variation in the dataset. This statistical method identifies the principal components linear combinations of original variables that capture maximum variance enabling data visualization, noise reduction, and computational efficiency in machine learning applications