Perform the following steps to split the churn dataset into training and testing datasets:

1. Load the rpart package:

        > library(rpart)  

2. Use the rpart function to build a classification tree model:

        > churn.rp = rpart(churn ~ ., data=trainset)  

3. Type churn.rp to retrieve the node detail of the classification tree:

        > churn.rp   

4. Next, use the printcp function to examine the complexity parameter:

        > printcp(churn.rp)
        Output

        Classification tree:
        rpart(formula = churn ~ ., data = trainset)

        Variables actually used in tree construction:
        [1] international_plan number_customer_service_calls
        [3] total_day_minutes total_eve_minutes 
        [5] total_intl_calls total_intl_minutes 
        [7] voice_mail_plan 

        Root node error: 342/2315 = 0.14773

        n= 2315 

                CP nsplit rel error xerror xstd
        1 0.076023 0 1.00000 1.00000 0.049920
        2 0.074561 2 0.84795 0.99708 0.049860
        3 0.055556 4 0.69883 0.76023 0.044421
        4 0.026316 7 0.49415 0.52632 0.037673
        5 0.023392 8 0.46784 0.52047 0.037481
        6 0.020468 10 0.42105 0.50877 0.037092
        7 0.017544 11 0.40058 0.47076 0.035788
        8 0.010000 12 0.38304 0.47661 0.035993  

5. Next, use the plotcp function to plot the cost complexity parameters:

        > plotcp(churn.rp)  

6. Lastly, use the summary function to examine the built model:

        > summary(churn.rp)