use python

aswer inly parts e and f!

Problem 2) Confusion matrix and metrics for measuring a model's performance: Assume that a classification model is trained to detect fraudulence emails from non-fraudulence ones, and we would like to test the classifier's efficacy in making such detections. A test data containing 40 email instances are provided. The labels of the test data are given and denoted as the gold- standard labels. The table below provides the gold-standard labels and the predicted labels using this classifier for each data sample. "0" means non-fraudulence emails, and "I" indicates fraudulence emails. Using this table, answer the following questions. instanse gold-standard labels 0 model-predicted labels 0 0 0 0 0 2 3 4 5 6 7 8 9 10 11 12 13 0 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 a) Calculate and show the confusion matrix. b) Calculate and provide the accuracy of the model. c) Calculate the sensitivity of the model. d) Calculate the specificity of the model. e) Calculate the F1 Measure of the model. f) Calculate the recall and precision