What is Principal Component Analysis (PCA)?
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Principal Component Analysis (PCA) is a method that reduces the dimensionality of data by transforming it into a new set of variables called principal components. These components are linear combinations of the original variables, ordered by their importance in explaining the data’s variation.
PCA simplifies data by identifying correlations between variables, reducing redundancy, and making it easier to analyze.
How Does PCA Work?
PCA works by finding the directions (principal components) where data varies the most—each component captures a specific pattern in the data. To achieve this, PCA involves:
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