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from pandas import read_csv
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression

# data
url = "https://goo.gl/vhm1eU"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]

# create fold object
kfold = KFold(n_splits=10, random_state=7)
# create algorithm object
model = LogisticRegression()
# validation
results = cross_val_score(model, X, Y, cv=kfold)
print('Accuracy: %.3f (%.3f)'%(np.mean(results)*100.0, np.std(results)*100.0))