Part 1. Clustering using shoeSales.csv You will be using the data set shoeSales.csv. This data set contains transactions from 250 customers, Each shows the type of shoes purchased (Athletic, Casual, Dress, Sandal, Work) and the amount spent. The goal is to find clusters of customers who have similar buying behaviors. (1) Read the data from shoeSales.csv and assign it to shoe data frame. (2) Run kmean=1. What is the total within cluster sum of squares? (3) Run kmeans 4(km4) to determine 4 clusters. Assign the result to km4 object. Use the following R code before running kmeans() function to get the same answer in each run. set. seed (125) (4) What is the total within cluster sum of squares with 4 clusters? (5) What percent of the total within sum of squares is explained by using 4 clusters? (6) How many customers are there in each cluster? (7) Display stores in the order of cluster numbers. To answer this question: - The vector km4\$C7uster displays the cluster number for each customer. Display this vector. - Add this vector km4\$C7 uster as a new column to shoe data frame. (Shoe is the data frame that you read shoeSales data into in Question 1). shoe\$c7uster