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 ...
