Customer Segmentation: Case Study

Data Science Case Study on Customer Segmentation

Download the dataset below to solve this Data Science case study on Customer Segmentation. (Dataset Source: Kaggle)

Customer Segmentation: Case Study

Customer segmentation means grouping customers based on their actions, interests, and buying habits. For example, a credit card company can group its customers based on their credit limit.

Here is a dataset based on customer segmentation submitted by Arjun Bhasin on Kaggle. Below are all the features in the dataset:

  1. CUST_ID: Unique identification number of the customer
  2. BALANCE: Balance in the bank account of the customer
  3. BALANCE_FREQUENCY: How frequently the balance is updated in the account of the customer (1 means frequently updated, and 0 means not frequently updated)
  4. PURCHASES: The number of purchases made by the customer
  5. ONEOFF_PURCHASES: Maximum amount of one-time purchase
  6. INSTALLMENTS_PURCHASES: Amount of purchases on instalments
  7. CASH_ADVANCE: Cash in advance paid by the customer
  8. PURCHASES_FREQUENCY: The frequency of purchases (1 means high frequency, 0 means low frequency)
  9. ONEOFF_PURCHASES_FREQUENCY: The frequency of one-time payment purchases (1 means high frequency, 0 means low frequency)
  10. PURCHASES_INSTALLMENTS_FREQUENCY: The frequency of purchases on instalments (1 means high frequency, 0 means low frequency)
  11. CASH_ADVANCE_FREQUENCY: Frequency of cash in advance payments
  12. CASH_ADVANCE_TRX: Number of cash in advance transactions
  13. PURCHASES_TRX: Number of transactions on purchases
  14. CREDIT_LIMIT: Credit limit of the customer
  15. PAYMENTS: Amount of payments made by the customer
  16. MINIMUM_PAYMENTS: Amount of minimum payments made by the customer
  17. PRC_FULL_PAYMENT: Percentage of full payment made by the customer
  18. TENURE: The tenure of the credit card service of the customer

This problem aims to group credit card customers based on their buying habits, credit limit, and other characteristics of the customers.

References to Solve this Data Science Case Study

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