The churnCredit data set contains \(10127\) rows (customers) and \(21\) columns (features). The churn column is our target which indicate whether customer churned (left the company) or not.
data(churnCredit)the churnCredit dataset, as a data frame, contains \(10127\) rows (customers) and \(21\) columns (variables/features). the \(21\) variables are:
customer.ID: Unique identifier for each account holder.
age: Age of the customer, in years.
gender: Gender of the account holder.
education: Educational qualification (high-school, college, graduate, uneducated, post-graduate, doctorate, unknown).
marital: Marital status (married, single, divorced, unknown).
income: Annual income bracket (less than $40K, $40K-$60K, $60K-$80K, $80K-$120K, over $120K, unknown).
card.category: Credit card type (blue, silver, gold, platinum).
dependent.count: Number of dependents.
months.on.book: Tenure with the bank, in months.
relationship.count: Total number of products held by the customer (1-6).
months.inactive: Number of inactive months in the past 12 months.
contacts.count.12: Number of customer service contacts in the past 12 months.
credit.limit: Total credit card limit.
revolving.balance: Current revolving balance on the credit card.
available.credit: Available credit line, representing the unused portion of the credit limit. Calculated as credit.limit - revolving.balance.
transaction.amount.12: Total transaction amount in the past 12 months.
transaction.count.12: Total number of transactions in the past 12 months.
ratio.amount.Q4.Q1: Ratio of total transaction amount in the fourth quarter to that in the first quarter.
ratio.count.Q4.Q1: Ratio of total transaction count in the fourth quarter to that in the first quarter.
utilization.ratio: Average credit utilization ratio, defined as revolving.balance / credit.limit.
churn: Indicator of whether the account was closed (yes) or remained active (no).
For more information related to the dataset see:
https://www.kaggle.com/sakshigoyal7/credit-card-customers
Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.
bank,
churn,
churnTel,
adult,
risk,
cereal,
advertising,
marketing,
drug,
house,
housePrice,
redWines,
whiteWines,
insurance,
caravan,
fertilizer,
corona
data(churnCredit)
str(churnCredit)
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