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.

This exercise is part of the course

Machine Learning for Marketing in Python

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Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create empty sse dictionary
sse = {}

# Fit KMeans algorithm on k values between 1 and 11
for k in ___(1, 11):
    kmeans = ___(n_clusters=___, random_state=333)
    kmeans.___(wholesale_scaled_df)
    sse[k] = kmeans.inertia_

# Add the title to the plot
plt.___('Elbow criterion method chart')

# Create and display a scatter plot
sns.pointplot(x=list(sse.___()), y=list(sse.___()))
plt.___()

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