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Principal Component Analysis (PCA) is a dimensionality reduction technique that is used to transform a set of correlated variables into a set of uncorrelated variables, called principal components. These principal components capture the most important information in the data while reducing the number of variables to a smaller number of components that explain most of the variance in the data. PCA is commonly used in data pre-processing and feature selection in various fields such as image processing, bioinformatics, and natural language processing.