Plot sum of squared errors
Now you will plot the sum of squared errors for each value of k and identify if there is an elbow. This will guide you towards the recommended number of clusters to use.
The sum of squared errors is loaded as a dictionary called sse from the previous exercise. matplotlib.pyplot was loaded as plt, and seaborn as sns.
You can explore the dictionary in the console.
Este exercício faz parte do curso
Customer Segmentation in Python
Instruções do exercício
- Add the plot title "The Elbow Method".
- Add the X-axis label "k".
- Add the Y-axis label "SSE".
- Plot SSE values for each
kstored as keys in the dictionary.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Add the plot title "The Elbow Method"
plt.____('The Elbow Method')
# Add X-axis label "k"
plt.____('____')
# Add Y-axis label "SSE"
plt.____('____')
# Plot SSE values for each key in the dictionary
sns.____(x=list(sse.____()), y=list(sse.____()))
plt.show()