Principal Component Analysis

Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform a high-dimensional dataset into a lower-dimensional one while preserving as much of the original variance as possible. PCA can be used to reduce the number of features in a dataset, visualize high-dimensional data, or preprocess data before applying other machine learning algorithms.