python code for evaluation part thank you

Evaluation of Your Clustering Model And this need for further investigation brings you to the next essential step, which is the evaluation of your model's performance. In other words, you want to analyze the degree of correctness of the model's predictions - in classification analysis, we use confusion matrix for that. Let's print out a confusion matrix: [] 1 \# Import 'confusion_matrix' from'sklearn.metrics' 2 3 4 \# Print out the confusion matrix with 'confusion_matrix()' 5 \# the 'confusion_matrix()' should be between 'y_test' and ' y ' pred' 6 You will want higher values to appear in the top-left to lower-right diagonal line of the matrix. Answer the question: Observe the confusion matrix above - which digit(s) are predicted well? Use the block below to provide your answer. Double click and type your answer here