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.

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Concept of memory addresses in pointers

The concept of pointers in high-level languages was introduced in C programming. Pointers are simply the variables that are used to store memory addresses of another variable. These special variables are generally used in the languages such as C Programming and C++ Programming, where it allows the programmer to work more deeply with memory and its addresses.

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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.

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