SVM with linear kernel and C=1 for Iris Data

svm_iris.py

from sklearn import datasets, svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
iris = datasets.load_iris()
X = iris.data[:, :2]
y = iris.target
#Split the data into test and train
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)  

# Linear Kernel
svc_linear = svm.SVC(kernel='linear', C=1)
svc_linear.fit(X_train, y_train)
predicted= svc_linear.predict(X_test)
cnf_matrix = confusion_matrix(y_test, predicted)
print(cnf_matrix)

# Output

[[16  0  0]
 [ 0 13  5]
 [ 0  4  7]]