Answer Posted / Rakesh Kumar Singh

Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. In machine learning, PCA is often used for dimensionality reduction and feature extraction.

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