More specifically, the reason why it is critical to perform standardization prior to PCA, is that the latter is quite sensitive regarding the variances of the initial variables. The aim of this step is to standardize the range of the continuous initial variables so that each one of them contributes equally to the analysis. Step-by-Step Explanation of PCA Step 1: Standardization So, to sum up, the idea of PCA is simple - reduce the number of variables of a data set, while preserving as much information as possible. Because smaller data sets are easier to explore and visualize and make analyzing data much easier and faster for machine learning algorithms without extraneous variables to process. Reducing the number of variables of a data set naturally comes at the expense of accuracy, but the trick in dimensionality reduction is to trade a little accuracy for simplicity. Principal component analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set. | Video: Visually Explained What Is Principal Component Analysis? An overview of principal component analysis (PCA).

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