#calculating accuracy metrix
#accuracy = (correct_predictions / total_predictions) * 100

# Example of calculating classification accuracy
# Calculate accuracy percentage between two lists
def accuracy_metric(actual, predicted):
    correct = 0
    for i in range(len(actual)):
        if actual[i] == predicted[i]:
            correct += 1
    return correct / float(len(actual)) * 100.0

# Test accuracy
if __name__ == '__main__':
    actual = [0,0,0,0,0,1,1,1,1,1]
    predicted = [0,1,0,0,0,1,0,1,1,1]
    accuracy = accuracy_metric(actual, predicted)
    print(accuracy)