emrecanduran / sas-enterprise-miner-customer-credit-card-k-means-clustering Goto Github PK

This repository contains code and data for performing K-means clustering on a customer credit card information dataset (https://www.kaggle.com/datasets/aryashah2k/credit-card-customer-data). The goal is to group customers based on their behaviour and identify patterns or segments within the customer base.

The dataset used for this analysis consists of the following variables:

• SI_No: Customer Serial Identification Number (unique)
• Customer Key: Customer Reference Key (unique)
• AvgCreditLimit: Average Credit Card Limit for the Customer
• Total_Credit_Cards: Total Credit Cards Owned by the Customer
• Total_visits_bank: Total Number of Bank Visits by the Customer
• Total_visits_online: Total Visits Online by the Bank Customer
• Total_calls_made: Total Calls Made by the Customer to the Bank

The K-means clustering algorithm was applied to the dataset using SAS Enterprise Miner. K-means is an unsupervised machine learning algorithm that partitions the data into K clusters based on the similarity of observations within each cluster. The algorithm iteratively assigns observations to clusters and updates the cluster centroids until convergence.

SAS Enterprise Miner 15.2 version

The clustering analysis resulted in 3 distinct customer segments based on their characteristics. [LowCreditHighVolumeCallCustomers, FrequentBankVisitors, TopCreditOnlineCustomers]