PCA (Principal Component Analysis) is a technique for reducing the dimensionality of data while preserving as much variability as possible. It transforms the original data into a new set of orthogonal variables called principal components, which are linear combinations of the original variables. The first principal component captures the maximum variance in the data, followed by the second principal component, and so on. PCA is widely used for data visualization, noise reduction, and feature extraction. By reducing the number of dimensions, PCA helps simplify complex datasets, making it easier to identify patterns and insights.