PRINCIPAL COMPONENT ANALYSIS
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Principal Component Analysis (PCA) is a valuable technique in multidimensional analysis for extracting meaningful insights from complex datasets.
PCA aids in identifying the primary variables contributing to process variations and reducing dimensionality. In this chapter, we will introduce Principal Component Analysis in Section 4.1. Section 4.2 will delve into the steps of this method. Section 4.3 will cover the most commonly used software and libraries for performing PCA. Lastly, in Sections 4.4 and 4.5, we will present
some practical examples along with their solutions.