Principal Component Analysis

+**Principal Component Analysis**

+Principal Component Analysis (PCA) is a fundamental statistical method for [Dimensionality Reduction](/wiki/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](/wiki/data_analysis) and visualization, making vast information more manageable for exploration and model building.

+## See also

+- [Linear Algebra](/wiki/linear_algebra)

+- [Feature Engineering](/wiki/feature_engineering)