we are using matplotlibit is the most well-known plotting package for python we present the code to generate the top-left plot the code for the other plots is similar to the following codefrom matplotlib import pyplot as plt from sklearn datasets import load_iris import numpy as np we load the data with load_iris from sklearn data load_iris(features data['data'feature_names data['feature_names'target data['target'for ,marker, in zip(xrange( ),">ox","rgb")we plot each class on its own to get different colored markers plt scatter(features[target = , ]features[target = , ]marker=markerc=cbuilding our first classification model if the goal is to separate the three types of flowerwe can immediately make few suggestions for examplethe petal length seems to be able to separate iris setosa from the other two flower species on its own we can write little bit of code to discover where the cutoff is as followsplength features[: use numpy operations to get setosa features is_setosa (labels ='setosa'this is the important stepmax_setosa =plength[is_setosamax(min_non_setosa plength[~is_setosamin(print('maximum of setosa{ format(max_setosa)print('minimum of others{ format(min_non_setosa)this prints and thereforewe can build simple modelif the petal length is smaller than twothis is an iris setosa flowerotherwiseit is either iris virginica or iris versicolor if features[:, print 'iris setosaelseprint 'iris virginica or iris versicolour