What is principal component analysis?

Principal Component Analysis (PCA) is a statistical technique used in data analysis and machine learning to reduce the dimensionality of large datasets. It works by transforming the original variables into a new set of uncorrelated variables called principal components, which are ordered by the amount of variance they capture from the data. PCA helps simplify data visualization, speeds up algorithms, and can improve model performance by eliminating noise and redundant features. This makes it particularly useful for exploratory data analysis and preprocessing before applying other machine learning algorithms.