Exercise

# Many K's many models

While the `lineup`

dataset clearly has a known value of **k**, often times the optimal number of clusters isn't known and must be estimated.

In this exercise you will leverage `map_dbl()`

from the `purrr`

library to run k-means using values of k ranging from 1 to 10 and extract the **total within-cluster sum of squares** metric from each one. This will be the first step towards visualizing the elbow plot.

Instructions

**100 XP**

- Use
`map_dbl()`

to run`kmeans()`

using the`lineup`

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**