0,Week,Date,B1,B2,B3,B4,B5,B6
1,1140,15/08/2018,3,10,15,18,25,43
2,1139,11/08/2018,9,15,16,21,32,33
3,1139,11/08/2018,9,15,16,21,32,33

dataset = pd.read_csv('son.csv')

# fit a Naive Bayes model to the data
model = GaussianNB()
y = dataset.values[:,9].reshape(1143,-1143)

X_train, X_test, y_train, y_test = train_test_split(dataset, y, test_size=0.2)
model.fit(X_train, y_train)
print(model)
# make predictions
expected = X_test
predicted = model.predict(y_test)
# summarize the fit of the model
print(metrics.classification_report(expected, predicted))
print(metrics.confusion_matrix(expected, predicted))