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I won’t explain the details of it here, but for anyone unfamilar with it we can summarise the purpose of PCA as follows: “When faced with a large set of correlated variables, PCA allows us to summarize this set with a smaller number of representative variables that collectively explain most of the variability in the original set…PCA also serves as a tool for data visualization.” Essentially, PCA involves performing a linear mapping of the data to a lower-dimensional space such that it helps us understand where most of the variance came from in the original data