Pca Analysis Explained
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Pca Analysis Explained
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Principal component analysis PCA is a technique used to emphasize variation and bring out strong patterns in a dataset It s often used to make data easy to explore and visualize 2D example First consider a dataset in only two dimensions like height weight This dataset can be plotted as points in a plane Principal Components Analysis PCA is an algorithm to transform the columns of a dataset into a new set of features called Principal Components By doing this a large chunk of the information across the full dataset is effectively compressed in fewer feature columns
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