Understanding clusters as customer segments

To rigorously evaluate the output obtained, we need to evaluate the depicted clusters. This is because clustering is an unsupervised method and the patterns extracted should always reflect reality, otherwise; we might just as well be analyzing noise.

Common traits among consumer groups can help a business choose which items or services to advertise to which segments and how to market to each one.

To do that, we will use exploratory data analysis (EDA) to look at the data in the context of clusters and make judgments. Here are the steps:

1. Let us first examine the clustering group distribution:

   cluster_count = PCA_ds["Clusters"].value_counts().reset_index()

   cluster_count.columns  = ['cluster','count']

   f, ax = plt.subplots(figsize=(10, 6))

   fig = sns.barplot(x="cluster", y="count", palette=['red','blue','green','orange'],data=cluster_count...