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R Data Analysis Cookbook - Second Edition by Kuntal Ganguly

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Principal Component Analysis

Principal Component Analysis (PCA) is a statistical technique for dimensionality reduction that transforms data with high-dimensional space into low-dimensional space. Its goal is to replace a large number of correlated variables with a smaller number of uncorrelated variables while retaining as much information in the original variables as possible, hence playing a significant role in feature engineering tasks of the machine learning pipeline.

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