* Example R codingwould be helpful

Market Segmentation for Hubway [AE, pp. 416-417] Market segmentation is a strategy that divides a broad target market of customers into smaller, more similar groups, and then designs a marketing strategy specifically for each group. Clustering is a common technique for market segmentation since it automatically finds similar groups within a given dataset. In this problem, we will see how clustering can be used to find similar groups of registered users of the Boston, Massachusetts bike-sharing program Hubway. Users of Hubway can check out a bicycle from one of 140 stations located throughout the Boston area, and return the bike to a different station. Users pay a membership fee, which includes unlimited trips up to 30 minutes in duration at no additional cost. Trips longer than 30 minutes cost additional overtime fees. The dataset HubwayTrips.csv contains data from trips taken by registered users of Hubway from June 2012 through September 2012. The dataset contains nine variables, described in Table 1. Our goal in this problem will be to cluster trips to ultimately find interesting insights about the types of trips customers of Hubway tend to take.

(b) Run the K-means clustering algorithm on your normalized dataset with 10 clusters. Set the random seed as 99.

(i) How many trips are in each of the clusters?

(ii) Compare the clusters by looking at the mean values of the variables (the cluster centroids). Note that you may want to look at the unnormalized data, but you could also look at the normalized data (whichever is more interpretable to you). How would you describe each cluster to the marketing department at Hubway?

(iii) Do you think some clusters are more interesting than others? Which ones? Why?

(iv) Looking at these results, do you think it would be helpful to have more clusters or fewer clusters than 10? Why?