Please use the file path below and Orange data mining software to complete questions a, b, c, and d

https://uiowa.instructure.com/courses/216942/files/23138871/download?download_frd=1

Using the data in ChurnImbalanced.csv, construct the following classification models to classify a customer observation as "leave" or "stay." Note that the primary target class of interest is the "leave" category as the phone company would like to intervene and retain these customers. Split the data so that 80% is used for training/validation in a 10-fold cross-validation experiment and 20% is used for a test set.

a) Use lasso regularization in conjunction with 10-folds cross-validation to evaluate and select a logistic regression model. Report the value of the lasso penalization determined in the cross-validation experiment and the corresponding value of the AUC. Then, construct your final model on all

of the training/validation data and report its performance measures (confusion matrix metrics, AUC, lift) on the test set.

b. Use ridge regularization in conjunction with 10-folds cross evaluate and select a logistic regression model. Report the ridge penalization determined in the cross-validation experiment corresponding value of the AUC. Then, construct your final of the training/validation data and report its performance measures (confusion matrix metrics, AUC, lift) on the test set.

c) Employ k-nearest neighbors in conjunction with 10-folds cross-validation to evaluate and select a classification model. Identify the value of k that results in the largest value of AUC. Then, construct your final model on all of the training/validation data and report its performance measures (confusion matrix metrics, AUC, lift) on the test set.

d) Compare the classification models in parts (a), (b), and (c).