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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 This graph represents the confusion matrix, that shows the correct and incorrect classifications of the decision tree, the labels means the life expectancy greater and less than 69 years, the horizontal labels are the predictions and the vertical are the reference (true).
the main diagonal
Step1:  The accuracy score is approximately 0.72, this suggests that the decision tree model has classified 72% of the sample as either countrie with life expectancy greater or less than the mean (69 years).
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Python Code:
accuracy = sklearn.metrics.accuracy_score(tar_test, predictions)
print ('Accuracy Score: ', accuracy,'\n')
Explanation: This graph represents the confusion matrix, that shows the correct and incorrect classifications of the decision tree, the labels means the life expectancy greater and less than 69 years, the horizontal labels are the predictions and the vertical are the reference (true).
the main diagonal: 16 and 34 represent the number of true negative and true positive for life expectancy and the other diagonal (values 7 and 12) represents the numbers of false negative and false positive respectively.
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tree.export_graphviz(clf, out_file=out, feature_names=['alcohol', 'income'],class_names=['0','1'], 
                     filled=True, rounded=True, special_characters=True)
graph=pydotplus.graph_from_dot_data(out.getvalue())
Image(graph.create_png())
print (data0.mean())
print (cat1)
Explanation: The accuracy score is approximately 0.72, this suggests that the decision tree model has classified 72% of the sample as either countrie with life expectancy greater or less than the mean (69 years).
End of explanation
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