Cyber security analytics -Model evaluation metrics python 3

In this task(see the attached full document task), you are given a dataset “[login to view URL]”, try to find the “best” classification model by comparing the evaluation metrics, especially the recall rates produced by knn, decision tree and random forest models. You are given: • Dataset: [login to view URL] • thresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9] • Parameter grid (param_grid): For knn, n_neighbors = [1, 2, 3, 4, 5] For decision tree, max_depth = [3, 4, 5, 6, 7] For random forest, n_estimators = [5, 10, 20, 50] • GridSearchCV(model_classifier(random_state=0), {param: param_grid}, cv=5, scoring='recall') • Other parameters of your setting You are asked to: • use the train and test sets split in practical10 (X_train, X_test, y_train, y_test, and X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample) • use Grid search with cross-validation to fit the undersample data with model knn, decision tree and random forest, respectively, set cv=5 • find and print the best parameter for each model (knn, decision tree or random forest) for X_train_undersample dataset • for each model, build classifier using the found best parameter, predict using test sets (X_test_undersample and X_test), and plot the confusion matrix for the two predictions. • for each model, plot recall matric for different threshold for the undersample dataset • for each model, plot precision-recall curve for the undersample dataset