Step $2$: Classification Models

Using the code discussed in the lecture, split the data into training and testing data sets.

Do not use TARGET$\_$LOSS$\_$AMT to predict TARGET$\_$BAD$\_$FLAG.

Create a LOGISTIC REGRESSION model using ALL the variables to predict the variable TARGET$\_$BAD$\_$FLAG

Create a LOGISTIC REGRESSION model and using BACKWARD VARIABLE SELECTION.

Create a LOGISTIC REGRESSION model and using a DECISION TREE and FORWARD STEPWISE SELECTION.

List the important variables from the Logistic Regression Variable Selections.

Compare the variables from the logistic Regression with those of the Random Forest and the Gradient Boosting.

Using the testing data set, create a ROC curves for all models. They must all be on the same plot.

Display the Area Under the ROC curve $($AUC$)$ for all models.

Determine which model performed best and why you believe this.

Write a brief summary of which model you would recommend using. Note that this is your opinion. There is no right answer. You might, for example, select a less accurate model because it is faster or easier to interpret.