How to do it...

1. Start with all the imports and load the iris dataset:

from sklearn import datasets

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

#load the classifying models
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC

iris = datasets.load_iris()
X = iris.data[:, :2]  #load the first two features of the iris data 
y = iris.target #load the target of the iris data

#split the whole set one time
#Note random state is 7 now
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=7)

#split the training set into parts
X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(X_train, y_train, test_size=0.25, random_state=7)

1. Create an instance of an SVC classifier and fit it:

svc_clf = SVC(kernel = 'linear',random_state = 7)
svc_clf.fit(X_train_2, y_train_2)

1. Do the same for logistic regression (both lines for logistic regression are compressed into one):

lr_clf = LogisticRegression(random_state = 7).fit(X_train_2, y_train_2)

1. Now predict and examine the SVC and logistic regression's performance on X_test_2:

svc_pred = svc_clf.predict(X_test_2)
lr_pred = lr_clf.predict(X_test_2)

print "Accuracy of SVC:",accuracy_score(y_test_2,svc_pred)
print "Accuracy of LR:",accuracy_score(y_test_2,lr_pred)

Accuracy of SVC: 0.857142857143
Accuracy of LR: 0.714285714286

1. The SVC performs better, but we have not yet seen the original test data. Choose SVC over logistic regression and try it on the original test set:

print "Accuracy of SVC on original Test Set: ",accuracy_score(y_test, svc_clf.predict(X_test))

Accuracy of SVC on original Test Set:  0.684210526316