Exercise

# K-means: Elbow analysis

In the previous exercises you used the dendrogram to propose a clustering that generated 3 trees. In this exercise you will leverage the k-means elbow plot to propose the "best" number of clusters.

Instructions

**100 XP**

- Use
`map_dbl()`

to run`kmeans()`

using the`oes`

data for k values ranging from 1 to 10 and extract the**total within-cluster sum of squares**value from each model:`model$tot.withinss`

Store the resulting vector as`tot_withinss`

- Build a new data frame
`elbow_df`

containing the values of k and the vector of**total within-cluster sum of squares** - Use the values in
`elbow_df`

to plot a line plot showing the relationship between**k**and**total within-cluster sum of squares**