DM9

from sklearn import datasets

cancer = datasets.load_breast_cancer()

print("Features: ", cancer.feature_names)

print("Labels: ", cancer.target_names)

cancer.data.shape

print(cancer.data[0:5])

print(cancer.target)

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target,
test_size=0.3,random_state=109) # 70% training and 30% test

from sklearn import svm

clf = svm.SVC(kernel='linear') # Linear Kernel

clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)

from sklearn import metrics

print("Accuracy:",metrics.accuracy_score(y_test, y_pred))