Principal Component Analysis

Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction while preserving as much variance as possible. It transforms a dataset into a set of orthogonal components, which are linear combinations of the original features. The first few principal components capture the majority of the variance in the data, allowing for simplified data representation. PCA is commonly used in exploratory data analysis, image compression, and preprocessing data for machine learning models to enhance performance and reduce computation time.