Click on this message to get the complete output. [63] : from sklearn.model_selection import train_test_split [65] : #Ste up input variables(X) and output variable(y) [67]: X = breast_cancer_data.drop( 'class', axis=1) y = breast_cancer_data[ ' class' ] import seaborn as sns [69] : import seaborn as sns [71] : #We examine correlated input variables to see if we should drop any [73]: cormat = X. corr() round (cormat, 2) #print(cormat); sns . heatmap (cormat); mean radius - 1.0 mean perimeter - mean smoothness - 0.8 mean concavity mean symmetry 0.6 radius error perimeter error 0.4 smoothness error concavity error symmetry error 0.2 worst radius worst perimeter 0.0 worst smoothness worst concavity -0.2 worst symmetry radius error worst radius mean radius concavity error symmetry error perimeter error worst concavity worst perimeter mean concavity mean perimeter worst symmetry mean symmetry smoothness error worst smoothness mean smoothness