The wine data set athttps://archive.ics.uci.edu/ml/datasets/wine (Links to an externalsite.) has 13 features. Develop in Python and apply your ownversion of the PCA algorithm to this data set, to visualize how PCAhelps with dimensionality reduction. Explain how many PrincipalComponents you will choose and why. What percent of the variance inthe data do the selected Principal Components cover? For theimplementation, you may use any objects, modules, and functions inNumPy, SciPy and other python libraries to do various operationssuch as to compute the eigen values, vectors or perform any othermath / linear algebra operation, but not use the PCA functionavailable in SciKit-Learn directly.
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