Build segmentation with k-means clustering

In this exercise, you will build the customer segmentation with KMeans algorithm. As you've identified in the previous step, the mathematically optimal number of clusters is somewhere around 3 and 4. Here, you will build one with 4 segments.

The pre-processed dataset has been loaded as wholesale_scaled_df. You will use it to run the KMeans algorithm, and the raw un-processed dataset as wholesale - you will later use it to explore the average column values for the 4 segments you'll build.

Import the KMeans algorithm from sklearn.cluster module.
Initialize KMeans algorithm with 4 clusters and a random state set to 123.
Fit the model on the pre-processed wholesale_scaled_df dataset.
Assign the generated labels to a new column called segment in the raw wholesale dataset

Exercise

Build segmentation with k-means clustering

Instructions