This assignment must be done in R. The purpose of this assignment is to perform classification usingregression trees, interpret the results, and analyze whether or not theinformation generated can be used to address a specific businessproblem.

For this assignment, you will use the "Credit Card Defaults" data setfrom the Topic Materials. Most data categories are self-explanatory.Clarifying notes are as follows.

1. Limit_Balance: Balance limit on the credit card
2. Sex: Gender
3. Education: Highest level of education completed
4. Pay_Status1-3: Pay status in the previous 1 to 3 months, respectively
5. Pay_Amt_Prev1-3: Amount paid in the previous 1 to 3 months, respectively
6. Bill_Amt_Prev1-3: Amount billed in the previous 1 to 3 months, respectively
7. Default: Whether or not the individual defaulted on their credit card payment

You are an analyst for a credit card company. Management wants toknow if there are any early signs that indicate whether customers willdefault on their credit cards. If these indicators can be identified,then more scrutiny can be placed on customer transactions in an effortto avoid losses. The rules to detect the potential for default must besimple enough for management to understand and easily implemented aspart of the early default flagging system. Your task is to determine theindicators and communicate your findings to management.

Question 1: Partition the data to create a training data set (70%) and test data set (30%).

Question 2: Build a single classificationtree with the training data and Default as the target. Include the"Default Tree Model" output when submitting the answer.

1. Which variable(s) were used in the tree model?
2. How would you use the model to predict whether or not the customer will default?
3. What is the accuracy of the model when using the training and testdata? Include the "Misclassification Table" outputs when submitting theanswer.
4. Consider the following individual: Limit_Balance=5000, Sex=Male,Education=High School, Marital_Status=Married, Age=30, Pay_Status1=OnTime, Pay_Status2=On Time, Pay_Status3=2 Mths Late, Pay_Amt_Prev1=0,Pay_Amt_Prev2=0, Pay_Amt_Prev3=0, Bill_Amt_Prev1=5000,Bill_Amt_Prev2=2500, Bill_Amt_Prev3=100. Based on the classificationmodel, what is the predicted default outcome? Explain your answer.

Question 3: Predicting a default correctlyis more important than predicting a nondefault outcome. Therefore, thefocus of the modeling process should be weighted toward predictingdefaults accurately. One way to do this is by increasing the cost ofmisclassifying a true default.

Rerun the model, but increase the cost of misclassifying a truedefault by a factor of 5 vs. misclassifying a true nondefault as 1. Makesure to set the minimum change in impurity to 0.01. Include the"Default-weighted Tree Model" output when submitting the answer.

1. Which variable(s) were used in the tree model?
2. What is the accuracy of the model when using the training and testdata? Include the "Misclassification Table" outputs when submitting theanswer.
3. Consider the following individual: Limit_Balance=5000, Sex=Male,Education=High School, Marital_Status=Married, Age=30, Pay_Status1=OnTime, Pay_Status2=On Time, Pay_Status3=2 Mths Late, Pay_Amt_Prev1=0,Pay_Amt_Prev2=0, Pay_Amt_Prev3=0, Bill_Amt_Prev1=5000,Bill_Amt_Prev2=2500, Bill_Amt_Prev3=100. Based on the classificationmodel, what is the predicted default outcome? Explain your answer.

Question 4: Based on the two classification tree models, which one should be used if the goal is to more accurately predict defaulters?

Question 5: Based upon your analysis, whatare the indicators of whether or not customers will default on theircredit cards? Discuss how management can use this information as part ofthe early default flagging system. Present your finding in the form of a250-word executive summary that includes relevant data, charts, andtables to validate the conclusions presented.

General Requirements:

Submit the answers to Questions 1-4 and the executive summary as Word documents.

APA format is not required, but solid academic writing is expected.

This assignment uses a grading rubric. Please review the rubric priorto beginning the assignment to become familiar with the expectationsfor successful completion.

You are not required to submit this assignment to Turnitin.