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K-means segmentation averages

In this exercise, you will explore the average column values for a 3-segment solution with K-means. As part of the test & learn exploration process, visually inspecting the segmentation solutions is critical to identify the most business relevant option.

The seaborn as sns, and matplotlib.pyplot as plt. Also, we have run a 3-segment solution with K-means and loaded the dataset with assigned segment labels as wholesale_kmeans3 DataFrame.

This exercise is part of the course

Machine Learning for Marketing in Python

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Exercise instructions

  • Group by the segment label and calculate average column values.
  • Print the average column values per each segment.
  • Create a heatmap on the average column values per each segment.
  • Display the chart.

Hands-on interactive exercise

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

# Group by the segment label and calculate average column values
kmeans3_averages = wholesale_kmeans3.___(['___']).___().round(0)

# Print the average column values per each segment
print(___)

# Create a heatmap on the average column values per each segment
sns.___(___.T, cmap='YlGnBu')

# Display the chart
plt.___()
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