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R Data Analysis Cookbook - Second Edition by Kuntal Ganguly

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Reducing dimensionality with principal component analysis

The reduction of features can increase the efficiency of data processing and it is widely used in the fields of pattern recognition, text retrieval, and machine learning. PCA is the most widely used linear method in dealing with dimension reduction problems. It is useful when data contains lots of features and there is redundancy or correlation among these features

The stats package offers the prcomp function to perform PCA. This recipe shows you how to perform PCA using these capabilities.

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