This case requires developing a customer segmentation to define a marketing strategy. The sample Dataset summarizes the usage behavior of 8950 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.

Columns (Features):

• CUST_ID : Identification of Credit Card holder (Categorical)
• BALANCE : Balance amount left in their account to make purchases
• BALANCE_FREQUENCY : How frequently the Balance is updated, score between 0 and 1 (1 = frequently * * updated, 0 = not frequently updated)
• PURCHASES : Amount of purchases made from account
• ONEOFF_PURCHASES : Maximum purchase amount done in one-go
• INSTALLMENTS_PURCHASES : Amount of purchase done in installment
• CASH_ADVANCE : Cash in advance given by the user
• PURCHASES_FREQUENCY : How frequently the Purchases are being made, score between 0 and 1 (1 = * * * * frequently purchased, 0 = not frequently purchased)
• ONEOFF_PURCHASES_FREQUENCY : How frequently Purchases are happening in one-go (1 = frequently * purchased, 0 = not frequently purchased)
• PURCHASES_INSTALLMENTS_FREQUENCY : How frequently purchases in installments are being done (1 = * frequently done, 0 = not frequently done)
• CASH_ADVANCE_FREQUENCY : How frequently the cash in advance being paid
• CASH_ADVANCETRX : Number of Transactions made with "Cash in Advanced"
• PURCHASES_TRX : Numbe of purchase transactions made
• CREDIT_LIMIT : Limit of Credit Card for user
• PAYMENTS : Amount of Payment done by user
• MINIMUM_PAYMENTS : Minimum amount of payments made by user
• PRC_FULL_PAYMENT : Percent of full payment paid by user
• TENURE : Tenure of credit card service for user
• Target ==>>> We want to set Clusters