Credit Card Clustering

The data for this analysis was taken from Kaggle and we will use AuDaS to automatically identify the clusters.The DataThe credit card data has 17 attributres for each customer which include the balance (credit owed by the customer), cash advance (when a customer withdraws cash using the credit card), the customer’s credit limit, minimum payment, percentage of full payments and tenure.The data is fairly clean but has some missing values which are picked up automatically by AuDaS.After applying the data preparation advice suggested by AuDaS we are able to look at the histogram view of the credit card customers.Histogram view of the data in AuDaSClusteringNow that we have prepared the data we are going to build a clustering model using AuDaS..These customers are known as transactors as they pay little interest charges.Cluster 2This segment is characterised by customers who have high balances and cash advances and one of the lowest purchase frequencies and percentages of full payments which indicates that they are one of the most lucrative segments for the credit card provider..These are typically known as revolvers who might be using their credit card’s as a loan.Cluster 4This segment is characterised by customers with the highest credit limit and the highest percentage of full payments (39%)..These are prime customers that the credit card provider can entice to increase their spending habits by raising their credit limits even more.Cluster 5This segment includes fairly new customers (low tenures) who have a low balance and cash advance.. More details

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