PCA works by identifying the axes (principal components) that account for the most variance in the data. The steps involved include:
Standardizing the data Computing the covariance matrix Calculating the eigenvalues and eigenvectors Projecting the data onto the principal components
These steps help to transform the original variables into a set of new, uncorrelated variables (principal components) ordered by the amount of variance they explain.
