Principal Component Analysis
Principal Component Analysis (PCA) is a fundamental statistical method for Dimensionality Reduction, transforming complex, high-dimensional data into a simpler, more interpretable form. It identifies new, orthogonal axes—principal components—that successively capture the maximum variance of the original dataset, revealing underlying patterns. This technique streamlines Data Analysis and visualization, making vast information more manageable for exploration and model building.