This assignment must be done in R. The purpose of this assignment is to perform classification using
regression trees, interpret the results, and analyze whether or not the
information generated can be used to address a specific business
problem.For this assignment, you will use the "Credit Card Defaults" data set
from the Topic Materials. Most data categories are self-explanatory.
Clarifying notes are as follows.Limit_Balance: Balance limit on the credit cardSex: GenderEducation: Highest level of education completedPay_Status1-3: Pay status in the previous 1 to 3 months, respectivelyPay_Amt_Prev1-3: Amount paid in the previous 1 to 3 months, respectivelyBill_Amt_Prev1-3: Amount billed in the previous 1 to 3 months, respectivelyDefault: Whether or not the individual defaulted on their credit card paymentYou are an analyst for a credit card company. Management wants to
know if there are any early signs that indicate whether customers will
default on their credit cards. If these indicators can be identified,
then more scrutiny can be placed on customer transactions in an effort
to avoid losses. The rules to detect the potential for default must be
simple enough for management to understand and easily implemented as
part of the early default flagging system. Your task is to determine the
indicators 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 classification
tree with the training data and Default as the target. Include the
"Default Tree Model" output when submitting the answer.Which variable(s) were used in the tree model?How would you use the model to predict whether or not the customer will default?What is the accuracy of the model when using the training and test
data? Include the "Misclassification Table" outputs when submitting the
answer.Consider the following individual: Limit_Balance=5000, Sex=Male,
Education=High School, Marital_Status=Married, Age=30, Pay_Status1=On
Time, 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 classification
model, what is the predicted default outcome? Explain your answer.Question 3: Predicting a default correctly
is more important than predicting a nondefault outcome. Therefore, the
focus of the modeling process should be weighted toward predicting
defaults accurately. One way to do this is by increasing the cost of
misclassifying a true default.Rerun the model, but increase the cost of misclassifying a true
default by a factor of 5 vs. misclassifying a true nondefault as 1. Make
sure to set the minimum change in impurity to 0.01. Include the
"Default-weighted Tree Model" output when submitting the answer.Which variable(s) were used in the tree model?What is the accuracy of the model when using the training and test
data? Include the "Misclassification Table" outputs when submitting the
answer.Consider the following individual: Limit_Balance=5000, Sex=Male,
Education=High School, Marital_Status=Married, Age=30, Pay_Status1=On
Time, 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 classification
model, 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, what
are the indicators of whether or not customers will default on their
credit cards? Discuss how management can use this information as part of
the early default flagging system. Present your finding in the form of a
250-word executive summary that includes relevant data, charts, and
tables 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 prior
to beginning the assignment to become familiar with the expectations
for successful completion.You are not required to submit this assignment to Turnitin.
