Customer Credit Card Data
Analyzing Patterns and Trends in Credit Card Usage
@kaggle.fhabibimoghaddam_customer_credit_card_data
About this Dataset
Customer Credit Card Data
The “Customer Credit Card Data” dataset provides valuable insights into credit card usage patterns and financial behavior. Each record represents an individual credit card holder, and the dataset includes the following features:
| Id | Features | Description |
|---|---|---|
| 1 | Cust_Id: | Identification of credit card holder |
| 2 | Balance: | A credit card balance or Total amount left in their account to make purchases |
| 3 | Balance_Frequency: | How frequently the balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated) |
| 4 | Purchases: | Total amount of purchases made from account |
| 5 | One_Off_Purchases: | Maximum purchase amount done in one-go |
| 6 | Installments_Purchases: | Amount of purchase done in installment |
| 7 | Cash_Advance: | Cash in advance given by the user |
| 8 | Purchases_Frequency: | How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased) |
| 9 | One_Off_Purchases_Frequency: | How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased) |
| 10 | Purchases_Installments_Frequency: | How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done) |
| 11 | Cash_Advance_Frequency: | How frequently the cash in advance being paid |
| 12 | Cash_Advance_Trx: | Number of Transactions made with "Cash in Advanced" |
| 13 | Purchases_Trx: | Number of purchase transactions made |
| 14 | Credit_Limit: | Limit of Credit Card for user |
| 15 | Payments: | Total amount of payments done by user |
| 16 | Minimum_Payments: | Minimum amount of payments made by user |
| 17 | Prc_Full_Payment: | Percentage of full payment paid by user |
| 18 | Tenure: | Tenure of credit card service for user |
This dataset is valuable for analyzing credit card behavior, identifying trends, and building predictive models related to credit usage. Researchers, analysts, and financial institutions can leverage this data to gain deeper insights into customer profiles and optimize credit card services.