Answer / Anil Kumar Verma

If the components are not rotated in Principal Component Analysis (PCA), the resulting transformed space will still have the same principal components but they may not be orthogonal to each other. This means that the variance of the data may not be optimally explained. Rotating the components using techniques like Kaiser's normalization or varimax rotation helps to ensure that the principal components are mutually orthogonal and maximize the explained variance.

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