import sklearn
from sklearn import datasets
from sklearn import svm
from sklearn import metrics

# importing dataset
cancer = datasets.load_breast_cancer()

#print(cancer.feature_names)
#print(cancer.target_names)

x = cancer.data
y = cancer.target

x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x,y,test_size=0.2)

classes = ['malignant', 'benign']

# using Support Vector Classifier
#   kernel: algo/eq used to increase dimension for more accuracy
#   hyperplain
clf = svm.SVC(kernel="linear")
clf.fit(x_train,y_train)

predict = clf.predict(x_test)
acc = metrics.accuracy_score(y_test, predict)

print(acc)