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
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.___()