cent_transformed scaler inverse_transform(cluster_centers[ ]['cluster_center']print(pd dataframe(np exp(cent_transformed),columns=feature_vector)print("silhouette score for cluster {is {}format(icluster_centers[ ]['silhouette_score'])print(for number of clusters amount_log recency_log frequency_log silhouette score for cluster is for number of clusters amount_log recency_log frequency_log silhouette score for cluster is when we look at the results of the clustering processwe can infer some interesting insights consider the three-cluster configuration and try to understand the following insights we get three clusters with stark differences in the monetary value of the customer cluster is the cluster of high value customer who shops frequently and is certainly an important segment for each business in the similar way we obtain customer groups with low and medium spends in clusters with labels and respectively frequency and recency correlate perfectly to the monetary value based on the trend we talked about in figure - (high monetary-low recency-high frequencythe five-cluster configuration results are more surprisingwhen we go looking for more segmentswe find out that our high valued customer base is comprised of two subgroupsthose who shop often and with high amount (represented by cluster those who have decent spend but are not as frequent (represented by cluster this is in direct conflict with the result we obtain from the silhouette score matrix which says the fivecluster segments are less optimal then the three cluster segments of courseremember you must not strictly go after mathematical metrics all the time and think about the business aspects too besides thisthere could be more insights that are uncovered as you visualize the data based on these segments which might prove that in-fact the three-cluster segmentation was far better for instanceif you check the right-side plot in figure - you can see that segments with five clusters have too much overlap among themas compared to segments with three clusters