Explain principal component analysis (PCA).
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PCA is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It identifies the principal components, which are orthogonal vectors that capture the directions of maximum variance in the data.