Icon

20230407_​Pikairos_​JustKnimeIt_​Season2_​Challenge2_​SegmentationOfCreditCardCustomers

Credit Card company ABC maintains information about customer purchases and payments. The information is available for individual customers as Payments Info and Purchase Info. The company wants to segment the customers into three (3) clusters, so that marketing campaigns can be designed according to each cluster. You are asked to use both infos together to build a clustering model that adequately segments the customers. What patterns do customers in the same cluster have in common? Also, Information for newly registered customers is available. You are asked to assign cluster labels to newly registered customers using the trained clustering model, and then export the results into a CSV file. Do the assignments make sense? How do you assess their quality?

Challenge 02: Segmentation of Credit CardCustomers This workflow takes credit card customer data, partitions it intotraining (existing customers) and test (new customers) sets, andperforms k-Means clustering to assign the data into 3 clusters.Silhouette coefficients are calculated for each row, as well as themean silhouette coefficients for each cluster and overall (around0.7). The silhouette coefficient is is an indicator of how similarobjects are to their own clusters (cohesion) compared to otherclusters (separation), on a scale from -1 to +1. Therefore, an overallvalue around 0.7 shows that the clustering model is adequate andcan be applied to new customers. The Cluster Assigner nodedetermines which cluster each new customer belongs to and theresult is written as a CSV file in the workflow data area.Findings:Cluster: Customers who have made medium-high value payments buta variety of purchases.Cluster: Customers who have made low value purchases andpaymentsCluster: Customers who have made medium value purchases andpayments Create 3 ClusterswithRandom InitializationReadCC GENERALDataFilter In Columns:CUST_IDPURCHASESPAYMENTSTop = 70% ExistingCustomersBottom = 30% NewCustomersMin-MaxNormalizationDenormalizeValuesCalculate SilhouetteCoefficient for Each Row,As Well as the MeanSilhouette CoefficientPer Cluster and OverallUse ClusterModel to Assignthe New customersto One of the ClustersApply NormalizationModel to the NewCustomer DataDenormalize ValuesWrite New CustomerData to a CSV FileJoin AssignedCluster to OriginalDataVisualize Data ina Scatter Plotand as a SilhouettePlot k-Means File Reader Column Filter Partitioning Normalizer Denormalizer SilhouetteCoefficient Cluster Assigner Normalizer (Apply) Denormalizer CSV Writer Joiner Visualize Data Challenge 02: Segmentation of Credit CardCustomers This workflow takes credit card customer data, partitions it intotraining (existing customers) and test (new customers) sets, andperforms k-Means clustering to assign the data into 3 clusters.Silhouette coefficients are calculated for each row, as well as themean silhouette coefficients for each cluster and overall (around0.7). The silhouette coefficient is is an indicator of how similarobjects are to their own clusters (cohesion) compared to otherclusters (separation), on a scale from -1 to +1. Therefore, an overallvalue around 0.7 shows that the clustering model is adequate andcan be applied to new customers. The Cluster Assigner nodedetermines which cluster each new customer belongs to and theresult is written as a CSV file in the workflow data area.Findings:Cluster: Customers who have made medium-high value payments buta variety of purchases.Cluster: Customers who have made low value purchases andpaymentsCluster: Customers who have made medium value purchases andpayments Create 3 ClusterswithRandom InitializationReadCC GENERALDataFilter In Columns:CUST_IDPURCHASESPAYMENTSTop = 70% ExistingCustomersBottom = 30% NewCustomersMin-MaxNormalizationDenormalizeValuesCalculate SilhouetteCoefficient for Each Row,As Well as the MeanSilhouette CoefficientPer Cluster and OverallUse ClusterModel to Assignthe New customersto One of the ClustersApply NormalizationModel to the NewCustomer DataDenormalize ValuesWrite New CustomerData to a CSV FileJoin AssignedCluster to OriginalDataVisualize Data ina Scatter Plotand as a SilhouettePlot k-Means File Reader Column Filter Partitioning Normalizer Denormalizer SilhouetteCoefficient Cluster Assigner Normalizer (Apply) Denormalizer CSV Writer Joiner Visualize Data

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: LAMBERT
Created at: 2023-04-07
On NodePit since: 2023-04-14
Last update: 2024-11-18
Created with KNIME version: v4.7.2
Tags: JKISeason2-2Normalizerk-MeansCluster AssignerDenormalizerScatter Plot (Plotly)Python View

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!