Foundations of Data Science
Principal Component Analysis (PCA) is a statistical technique used to simplify complex datasets by reducing their dimensionality while retaining most of the original variance. This method transforms the data into a new set of variables, called principal components, which are uncorrelated and ordered by the amount of variance they capture from the original data. PCA is widely used in feature extraction, allowing for easier analysis and visualization of high-dimensional data.
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