K_Mean Model / GMM / Hierarichical clustering / Mean Shift / Affinity Propagtion / DBSCAN / " Heirarichicaal is the best model " " We choose the Heirarichicaal With Outliers as the final model "

About this Dataset The data source was taken from the Kaggle challenge called Credit Card Dataset for Clustering. The sample Dataset summarizes the usage behavior of about 9000 active credit cardholders during the last six months

The file is at a customer level with 18 behavioral variables.

Following is the Data Dictionary for Credit Card dataset:

CUSTID: Identification of Credit Cardholder (Categorical)

BALANCE: Balance amount left in their account to make purchases

BALANCEFREQUENCY: How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated)

PURCHASES: Amount of purchases made from the account

ONEOFFPURCHASES: Maximum purchase amount did in one-go

INSTALLMENTSPURCHASES: Amount of purchase done in installment

CASH ADVANCE: Cash in advance given by the user

PURCHASESFREQUENCY: How frequently the Purchases are being made score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased)

ONEOFFPURCHASESFREQUENCY: How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased)

PURCHASESINSTALLMENTSFREQUENCY: How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done)

CASHADVANCEFREQUENCY: How frequently the cash in advance being paid

CASHADVANCETRX: Number of Transactions made with “Cash in Advanced”

PURCHASESTRX: Number of purchase transactions made

CREDIT LIMIT: Limit of Credit Card for user

PAYMENTS: Amount of Payment done by the user

MINIMUM_PAYMENTS: Minimum amount of payments made by the user

PRCFULLPAYMENT: Percent of full payment paid by the user

TENURE: Tenure of credit card service for user