At the start of this course, you were divided into teams, and each team was assigned to a nation in the Five Eyes intelligence alliance. As a nation team, you collaborated to represent and defend the cybersecurity policy and posture of the nation assigned to you.Your team was also assigned to a critical infrastructure sector, which you will also represent in Project 4. Research for Project 4 is designed to span the entire term, and your team is expected to complete it in parallel with Projects 1 through 3.You will work with your team to prepare for and then complete the Executive Leadership In Tabletop Exercises (ELITE) activity, and then write a report that identifies risks to cyber networks within the critical infrastructure industry segment assigned to your team and proposes recommendations to mitigate risks and make the networks more secure. This project will span across the entire course, but should take about 24 days to complete. After reading the scenario below, proceed to Step 1 where you will establish your team agreement plan.This is the part of the project I will be writing!!!!!!!!!!Upon the conclusion of the second round of events, you have reviewed the results as a team. Now you will collaborate to create the second After Action Report (AAR) and make new decisions based on the new set of events for Round 3. Select decisions in response to the final events in ELITE by following these steps:Begin writing the Round 2 After Action Report in accordance to your team agreement and schedule.When you have completed AAR #2, a designated team member should submit it for feedback. This team member should also post the AAR in the ELITE Industries Sharing Discussion to share with the other sectors.Log in to ELITE to begin round 3.Review the newscasts and advisories for any new events.Meet with your team (and other teams if you have formed a multi-industry council) to review the Round 2 outcomes, reports, and debrief from the CISO. Collaborate on what new decisions need to be made.Make the new decisions in ELITE according to the responsibilities assigned to you in your team's ELITE agreement from a previous step. Because these decisions are tied to your login information, your team members will not be able to change or update these values. Final Round 3 values must be selected by 11:59 p.m. eastern time the day after the ELITE round begins. See your team schedule for details.

Details: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 card Sex: Gender Education: Highest level of education completed Pay_Status1-3: Pay status in the previous 1 to 3 months, respectively Pay_Amt_Prev1-3: Amount paid in the previous 1 to 3 months, respectively Bill_Amt_Prev1-3: Amount billed in the previous 1 to 3 months, respectively Default: Whether or not the individual defaulted on their credit card payment You 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.