Exercise

# Determine the optimal number of clusters

Here, you will use the elbow criterion method to identify the optimal number of clusters where the squared sum of error decrease becomes marginal. This is an important step to get a mathematical ball-park number of clusters to start testing. You will iterate through multiple `k`

number of clusters and run a `KMeans`

algorithm for each, then plot the errors against each `k`

to identify the "elbow" where the decrease in errors slows downs.

The `KMeans`

module is loaded from `sklearn.cluster`

, the `seaborn`

library is loaded as `sns`

, and the `matplotlib.pyplot`

module is loaded as `plt`

. Also, the scaled dataset is loaded as `wholesale_scaled_df`

as a `pandas`

DataFrame.

Instructions

**100 XP**

- Create an empty
`sse`

dictionary. - Fit a
`KMeans`

algorithm on k values between 1 and 11 and store the errors in the`sse`

dictionary. - Add the title to the plot.
- Create a scatter plot with keys on X-axis and values on the Y-axis and display the chart.