PCA
Principal Component Analysis is a dimensionality reduction algorithm that projects high-dimensional data onto lower-dimensional orthogonal axes of variance.
Think of It Like This
Like shining a flashlight on a complex 3D object to find the specific 2D shadow that captures the most recognizable shape.
PCA mathematically transforms correlated features into a smaller set of uncorrelated variables called principal components. The first component captures the maximum possible variance, and each subsequent orthogonal component captures the remaining variance. It is widely used for exploratory data analysis, visualization, and noise reduction.