The data in the Airlines data file contains data from 3,999 airline customers enrolled in East-West Airlines’ customer rewards program. (Note that while East-West Airlines is clearly fictional, this is data from a real airlines reward program; names have been changed to protect the innocent and not-so-innocent alike.) East-West Airlines has two goals with this analysis: (1) identifying if a customer will claim a travel award using their rewards, and (2) identifying factors that lead to customers claiming a travel award.  The data contains information about each customer’s history:

 

Field Name	Description
ID	Unique ID
Balance	Number of miles eligible for award travel
Qual_miles	Number of miles counted as qualifying for Topflight status
cc1_miles	Number of miles earned with freq. flyer credit card in the past 12 months:
cc2_miles	Number of miles earned with Rewards credit card in the past 12 months:
cc3_miles	Number of miles earned with Small Business credit card in the past 12 months:
note:  miles are binned	1 = under 5,000
	2 = 5,000 - 10,000
	3 = 10,001 - 25,000
	4 = 25,001 - 50,000
	5 = over 50,000
Bonus_miles	Number of miles earned from non-flight bonus transactions in the past 12 months
Bonus_trans	Number of non-flight bonus transactions in the past 12 months
Flight_miles_12mo	Number of flight miles in the past 12 months
Flight_trans_12	Number of flight transactions in the past 12 months
Days_since_enroll	Number of days since Enroll_date
Award	Dummy variable for travel award claimed (1 = award claimed, 0 = not claimed)

Include the relevant computer output you used to answer each question with your response.

• Data Preparation

1. Set the seed 123
2. Randomly partition the data set into training, validation, and testing data by:
   1. First, splitting off 25% of the data for testing
   2. Second, splitting off 25% of the remaining data for validation

• Third, setting the remaining data as training data
  
   
• Appropriate Data Analysis Techniques

1. Consider the problem of analyzing the East-West Airlines travel award data. Without running any analysis (yet), of all the methods we’ve learned in class, choose three that you think would be appropriate for this problem and for each method explain:
   1. Why you think this method is appropriate for the problem
   2. How you would use this method to address either Goal #1 for East-West Airlines, Goal #2 for East-West Airlines, or both

 

• Goal #1: Identifying if a customer will claim a travel award using their rewards

1. If East-West Airlines is most concerned with identifying if a new customer will claim a travel award using their rewards, what three methods would you suggest as the most appropriate for this problem? Why?
2. Run each of your three methods and decide which method East-West Airlines should use in practice. (Make sure to support your answers!)

 

• Goal #2: Identifying factors that lead to customers claiming a travel award
  
  1. If East-West Airlines is most concerned with identifying factors that lead to customers claiming a travel award, what three methods would you suggest as the most appropriate for this problem? Why?
  2. Run each of your three methods and decide which factors East-West Airlines should focus on for determining if customers will claim a travel award. (Make sure to support your answers!)