Solve In R using library$($ggdendro$)$

library$($tidyverse$)$

library$($dplyr$)$

set.seed$(1947)$ : The dataset East West Airlines Cluster contains information on $3999$ passengers who belong to an

airline's frequent flier program. For each passenger, the data include information on their mileage

history and on different ways they accrued or spent miles in the last year. The goal is to try to

identify clusters of passengers that have similar characteristics for the purpose of targeting

different segments for different types of mileage offers.

$1.$ Explore, prepare, and transform the data to facilitate predictive modeling. Describe your

process. $(5)$

$2.$ Apply hierarchical clustering with Euclidean distance and Ward's method. Make sure to

normalize the data first. How many clusters would you pick and why? $(5)$

$3.$ Compare the cluster centroid to characterize the different clusters and try to give each cluster

a label. $(5)$

$4.$ Use k$-$means clustering with the number of clusters that you found above. Does the same

picture emerge? $(5)$

$5.$ To check the stability of the clusters, remove a random $5\%$ of the data, and repeat the analysis.

Does the same picture emerge? $(5)$

$6.$ Which clusters would you target for offers, and what types of offers would you target to

customers in that cluster? $(5)$