Marketing is essential for brands to attract customers. One of the dwelling pain points of companies is to segment customers in a way so that marketers can lauch campagines targeting to particular segments.
The dataset used for this project is of a bank's customers having 8000+ records and 18 features. Some of the features are discussed below
- CUSTID: Identification of Credit Card holder
- BALANCE: Balance amount left in customer's 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
- ONEOFFPURCHASES: 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_ADVANCE_TRX: Number of Transactions made with "Cash in Advance"
- PURCHASES_TRX: Number 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
