Finding Redundancies

One of the important things that principal component analysis can do is shrink redundancy in your dataset. In its simplest manifestation, redundancy occurs when two variables are correlated.

The Pearson correlation coefficient is a number between -1 and 1. Coefficients near zero indicate two variables are linearly independent, while coefficients near -1 or 1 indicate that two variables are linearly related.

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