Principal Component Analysis, is one of the most useful dimensionality reduction techniques. It is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
In this video, I aim to explain how you can find out the n_components or number of principal components for a feature matrix by applying PCA from scratch and not using sklearn’s PCA module.
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