PCA 51 is a widely used linear multivariate statistical technique for dimensionality reduction, valued for its interpretability. As a matrix factorization method, PCA identifies patterns and extracts critical features from high-dimensional datasets by transforming the original variables into a new set of orthogonal, uncorrelated variables known as principal components. This process achieves a lower-dimensional representation of the data while retaining as much of the original information as possible. PCA is particularly effective when the primary variations in the data are linear, making it a good starting point for exploratory data analysis.