Answer Posted / Shobhit Tyagi

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. This is done in such a way that the first principal component has the largest possible variance, and each succeeding component in turn has the largest variance among the subspaces orthogonal to the space spanned by the preceding components.

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103

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