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Principal Component Analysis (PCA) is a machine-learning technique for reducing the dimensionality of data. It enjoys a number of uses in machine learning, from noise reduction to visualizing high-dimensional data using 2D and 3D plots. Learn what PCA is, how it works, and how to use it to build better machine-learning models.