Principal component analysis (PCA)

Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while retaining most of the variation in the data. It does this by identifying the principal components, which are the directions in which the data varies the most. PCA is commonly used in exploratory data analysis and for making predictive models more efficient.