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Just KNIME It S02 _​ CH02 _​ Segmentation of Credit Card Users

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?

Author: @mpattadkal



Just KNIME It - Season2 - Challenge02: Segmentation of Credit Card Users Handle missingvalues data CC GENERAL.csv$CREDIT_LIMIT$ @meanapply to $CUST_ID$ == C15349$MINIMUM_PAYMENTS$ @mean apply to NULL valuesdata column content descriptionupper: ln(boxplot) for the highest 9 correlated parametersfrom 'old TENURE' data...lower: K=3 k-Means clustered datasetalready concatenated old and new TENURE customersexport the results into a CSV file CSV Reader Missing Value Table Creator Segmentation ofCredit Card Users CSV Writer Just KNIME It - Season2 - Challenge02: Segmentation of Credit Card Users Handle missingvalues data CC GENERAL.csv$CREDIT_LIMIT$ @meanapply to $CUST_ID$ == C15349$MINIMUM_PAYMENTS$ @mean apply to NULL valuesdata column content descriptionupper: ln(boxplot) for the highest 9 correlated parametersfrom 'old TENURE' data...lower: K=3 k-Means clustered datasetalready concatenated old and new TENURE customersexport the results into a CSV file CSV Reader Missing Value Table Creator Segmentation ofCredit Card Users CSV Writer

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