Project: Data Mining on East-West airlines Case Solution
Data Description
The data which we used in our analysis comprises of 16 variables including continuous and categorical variables while there were almost 5,000 observers or respondents from whom the data has been collected and then the final analysis was performed.
Techniques Used
Several techniques were used to identify that who are the potential customers that are interested on the company’s products/services to increase their revenue and ultimately profits. However, this analysis is very important as, the company wants to analyze that on which customers it has to promotional. Therefore, the data which we took had the following variables.
| Field Name | Data Type | Max Data Length | Raw Data or Telkom Created Field? | Description |
| ID# | NUMBER | Telkom | Unique ID | |
| Topflight | CHAR | 1 | Raw | Indicates whether flyer has attained elite "Topflight" status, 1 = yes, 0 = no |
| Balance | NUMBER | 8 | Raw | Number of miles eligible for award travel |
| Qual_miles | NUMBER | 8 | Raw | Number of miles counted as qualifying for Topflight status |
| cc1_miles? | CHAR | 1 | Raw | Has member earned miles with airline freq. flyer credit card in the past 12 months (1=Yes/0=No)? |
| cc2_miles? | CHAR | 1 | Raw | Has member earned miles with Rewards credit card in the past 12 months (1=Yes/0=No)? |
| cc3_miles? | CHAR | 1 | Raw | Has member earned miles with Small Business credit card in the past 12 months (1=Yes/0=No)? |
| Bonus_miles | NUMBER | Raw | Number of miles earned from non-flight bonus transactions in the past 12 months | |
| Bonus_trans | NUMBER | Raw | Number of non-flight bonus transactions in the past 12 months | |
| Flight_miles_12mo | NUMBER | Raw | Number of flight miles in the past 12 months | |
| Flight_trans_12 | NUMBER | Raw | Number of flight transactions in the past 12 months | |
| Online_12 | NUMBER | Raw | Number of online purchases within the past 12 months | |
| CHAR | 1 | Raw | E-mail address on file. 1= yes, 0 =no? | |
| Club_member | NUMBER | Telcom | Member of the airline's club (paid membership), 1=yes, 0=no | |
| Any_cc_miles_12mo | NUMBER | Telcom | Dummy variable indicating whether member added miles on any credit card type within the past 12 months (1='Y', 0='N') | |
| Phone sale | NUMBER | Telcom | Dummy variable indicating whether member purchased Telcom service as a result of the direct mail campaign (1=sale, 0=no sale) |
In order to keep the analysis simple, we first used dimension reduction techniques such as factor analysis,however the results were not acceptable. As a result, the analyst used the following techniques to perform the final analysis.
- Correlation
- Two Step Cluster Analysis
- Descriptive Statistics
However, we used 4985 observations which were under consideration to avoid outliers and other data related issues. On the other hand, the analyst used some other technique to ensure that the data is reliable and contains no or minimum outliers.
Normality Check
In order to identify that the data is consistent and there are no or minimum outliers in the data, we used parallel lines and Q-Q plots from the Xlminer and found that there were no outliers and that the data is normal.
Correlation
The correlation was also calculated to identify that what is the relation between variables and either it exists or not, and if it does exist than what are its strength and its significance. The results of the regression analysis are shown below:
From appendix 1, it can be seen that the relation is either negative or positive among the variables however, the most significant positive relation has been found among Flight Miles 12mo and Flight Trans 12 as, both the effects are positively affecting each other and would affect one another by almost 83%. This indicates that if one mile is increased in flight miles 12mo, then flight Trans will be increased by 83%. However, these significant results have been made based on these variables and these are the most special and important variables among the overall regression model...........
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