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Today, we will discuss Principal Component Analysis (PCA), which is a technique used to reduce the dimensionality of a dataset. It is not a machine learning algorithm but rather a method to convert multiple independent features into a new vector space with fewer features. PCA can be applied to any number of independent features to reduce them to the desired number. This technique involves creating independent components based on the desired number of dimensions.
