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JKISeason2-2 Segmentation of Credit Card Users Jose De Souza

JKISeason2-2 Jose De Souza

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Challenge 02: Segmentation of Credit Card UsersLevel: MediumDescription: 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 customersinto 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 thesame 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? Reading the data Preprocessing the Data: Cleaning, normalizing90% of the dataSince we are using KMeands, we have to normalize our data and preprocess outliers Replicating the same preprocessing method Modelling: Data Clusterization throughKmeans Modelling: Data Clusterization throughKmeans Assigning cluster model to the newprocessed data Exporting Data to CSV Read Data, place Customer ID as Rows Removing Rowswith null valuesAnalizing Silhoutte ScoreHigher=BetterWe divide the dataso we can have a validation set or"new data" for assigning clustersThere are 314 Missing Values in 2 columns, not all columns follow a normal distributionReplacing Outlierswith closest permitted valueNormalizing data withZScoresReviewing distributionsGenerating 3 ClustersReducing to 2 PCNode 39 CSV Reader Missing Value SilhouetteCoefficient Partitioning Statistics Numeric Outliers Normalizer Statistics k-Means Viz Numeric Outliers Normalizer Missing Value Cluster Assigner CSV Writer PCA PCA Joiner Challenge 02: Segmentation of Credit Card UsersLevel: MediumDescription: 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 customersinto 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 thesame 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? Reading the data Preprocessing the Data: Cleaning, normalizing90% of the dataSince we are using KMeands, we have to normalize our data and preprocess outliers Replicating the same preprocessing method Modelling: Data Clusterization throughKmeans Modelling: Data Clusterization throughKmeans Assigning cluster model to the newprocessed data Exporting Data to CSV Read Data, place Customer ID as Rows Removing Rowswith null valuesAnalizing Silhoutte ScoreHigher=BetterWe divide the dataso we can have a validation set or"new data" for assigning clustersThere are 314 Missing Values in 2 columns, not all columns follow a normal distributionReplacing Outlierswith closest permitted valueNormalizing data withZScoresReviewing distributionsGenerating 3 ClustersReducing to 2 PCNode 39CSV Reader Missing Value SilhouetteCoefficient Partitioning Statistics Numeric Outliers Normalizer Statistics k-Means Viz Numeric Outliers Normalizer Missing Value Cluster Assigner CSV Writer PCA PCA Joiner

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Created by: USUARIO
Created at: 2023-03-29
On NodePit since: 2023-04-12
Last update: 2024-04-28
Created with KNIME version: v4.7.1
Tags: JustknimeitCorrelationJKISeason2-2

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