pcadat py import numpy as np np genfromtxt ('pcadat csv 'delimiter =',' shape [ mean(axis = u_ np linalg svd( /nprojected points np outer ( [, , [, ]import matplotlib pyplot as plt from mpl_toolkits mplot import axes fig plt figure (ax fig add_subplot ( projection =' 'ax w_xaxis set_pane_color (( )ax plot( [, [, [, =' 'linewidth = ax scatter ( [, [, [, =' 'ax scatter ( [, [, [, =' 'for in range ( )ax plot ([ [ , [ , ][ [ , , [ , ][ [ , , [ , ]' 'ax set_xlabel (' 'ax set_ylabel (' 'ax set_zlabel (' 'plt show (+ next is an application of pca to fisher' famous iris data setalready mentioned in section and exercise example (pca for the iris data setthe iris data set contains measurements on four features of the iris plantsepal length and widthand petal length and widthfor total of specimens the full data set also contains the species namebut for the purpose of this example we ignore it figure shows that there is significant correlation between the different features can we perhaps describe the data using fewer features by taking certain linear combinations of the original featuresto investigate thislet us perform pcafirst centering the data the following python code implements the pca it is assumed that csv file irisx csv has been made that contains the iris data set (without the species informationpcairis py import seaborn as sns numpy as np np set_printoptions precision = np genfromtxt ('irisx csv ',delimiter =',' shape [