QUESTION 2 You don't have to plot the points as characters; you can use different colors/shapes as long as you include a legend. USE PYTHON AND HANDWRITE BOTH METHODS Data: https://archive.ics.uci.edu/ml/datasets/Wine Take the wine dataset from the UC Irvine machine learning data repository at https://archive.ics.uci.edu/ml/datasets/Wine (a) Plot the eigenvalues of the covariance matrix in sorted order. How many principal components should be used to represent this dataset? Why? (c) Compute the first two principal components of this dataset, and project it onto those components. Now produce a scatter plot of this two dimensional dataset, where data items of class 1 are plotted as a '1', class 2 as a '2', and so on.

QUESTION 2 You don't have to plot the points as characters; you can use different colors/shapes as long as you include a legend. USE PYTHON AND HANDWRITE BOTH METHODS Data: https://archive.ics.uci.edu/ml/datasets/Wine Take the wine dataset from the UC Irvine machine learning data repository at https://archive.ics.uci.edu/ml/datasets/Wine (a) Plot the eigenvalues of the covariance matrix in sorted order. How many principal components should be used to represent this dataset? Why? (c) Compute the first two principal components of this dataset, and project it onto those components. Now produce a scatter plot of this two dimensional dataset, where data items of class 1 are plotted as a '1', class 2 as a '2', and so on.