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Using the SVC algorithm implemented by the Python Scikit-learn, classify the three types of flowers (Setosa, Versicolor, Virgin) in Iris dataset according to the Petal length and width

May I know how to modify my Python programming as refer to the attached file -

# To Get iris dataset
from sklearn import datasets 
# To fit the svm classifier
from sklearn import svm 
import numpy as np
import matplotlib.pyplot as plt 

iris_dataset = datasets.load_iris()

def visuvalise_petal_data():
 iris = datasets.load_iris()
 # Only take the first two features
 X = iris.data[:, 2:3] 
 y = iris.target

visuvalise_petal_data()
iris = datasets.load_iris()
# Only take the Sepal two features
X = iris.data[:, 2:3] 
y = iris.target
# SVM regularization parameter

# SVC with rbf kernel
rbf_svc = svm.SVC(kernel='rbf', gamma=0.01, C=1).fit(X, y)
rbf_svc = svm.SVC(kernel='rbf', gamma=0.01, C=10).fit(X, y)
# step size in the mesh
h = 0.02 
# create a mesh to plot in
def plotSVC(title):
 x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
 h = (x_max / x_min)/100
 xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
 np.arange(y_min, y_max, h))
 plt.subplot(1, 1, 1)
 Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])
 Z = Z.reshape(xx.shape)

C = [1, 10] 
for c in cs:
 svc = svm.SVC(kernel='rbf', C=1).fit(X, y)
 svc = svm.SVC(kernel='rbf', C=10).fit(X, y)
 plotSVC('C=' + str(c))

from sklearn.svm import SVC 
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 100, random_state = 0)

sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

linear_svm1 = SVC(kernel = 'rbf', C = 1, random_state = 0)
linear_svm1.fit(X_train_std, y_train)
y_predict1 = linear_svm1.predict(X_test_std)
print('Gamma=0.01,C=1')

linear_svm2 = SVC(kernel = 'rbf', C = 10, random_state = 0)
linear_svm2.fit(X_train_std, y_train)
y_predict2 = linear_svm2.predict(X_test_std)
print('Gamma=0.01,C=10')

svm = SVC(kernel='linear', C=1.0, random_state=0)
svm.fit(X_train_std, y_train)
plot_decision_regions(X, y, classifier=svm, test_idx=range(105,150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()

The error message is -

runfile('C:/Users/HSIPL/Desktop/Homework 6 Solution draft.py', wdir='C:/Users/HSIPL/Desktop')
Traceback (most recent call last):

 File "<ipython-input-85-761bed922ac3>", line 1, in <module>
 runfile('C:/Users/HSIPL/Desktop/Homework 6 Solution draft.py', wdir='C:/Users/HSIPL/Desktop')

 File "C:UsersHSIPLAnaconda3libsite-packagesspyder_kernelscustomizespydercustomize.py", line 668, in runfile
 execfile(filename, namespace)

 File "C:UsersHSIPLAnaconda3libsite-packagesspyder_kernelscustomizespydercustomize.py", line 108, in execfile
 exec(compile(f.read(), filename, 'exec'), namespace)

 File "C:/Users/HSIPL/Desktop/Homework 6 Solution draft.py", line 44, in <module>
 plotSVC('C=' + str(c))

 File "C:/Users/HSIPL/Desktop/Homework 6 Solution draft.py", line 32, in plotSVC
 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1

IndexError: index 1 is out of bounds for axis 1 with size 1

Please help so that I can improve my computing skills