Build 4-cluster solution

Perfect, you can see the the recommended number of clusters is somewhere between 3 and 4. Now, you will build the latter number of clusters in this exercise.

The normalized RFMT dataset is available as datamart_rfmt_normalized, feel free to use the console to explore it.

This exercise is part of the course

Customer Segmentation in Python

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Exercise instructions

Import KMeans from sklearn library.
Initialize KMeans with 4 clusters and random state 1.
Fit k-means clustering on the normalized data set.
Extract cluster labels and store them as cluster_labels object.

Hands-on interactive exercise

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

# Import KMeans 
from ____.____ import ____

# Initialize KMeans
kmeans = ____(____, ____) 

# Fit k-means clustering on the normalized data set
____.____(____)

# Extract cluster labels
cluster_labels = ____.____

Edit and Run Code