Hands-On Data Science for Marketing

Interpreting customer segments

In this section, we are going to discuss different ways to draw insights from the results of the previous clustering analysis. Let's first build a k-means clustering model with four clusters. You can use the following code:

kmeans = KMeans(n_clusters=4).fit(    normalized_df[['TotalSales', 'OrderCount', 'AvgOrderValue']])four_cluster_df = normalized_df[['TotalSales', 'OrderCount', 'AvgOrderValue']].copy(deep=True)four_cluster_df['Cluster'] = kmeans.labels_

As you can see from this code, we are fitting a k-means clustering model with 4 clusters, based on three attributes: TotalSales, OrderCount, and AvgOrderValue. Then, we store the cluster label information into a DataFrame, four_cluster_df. This DataFrame is shown ...