The churn_tel data set contains \(7043\) rows (customers) and \(21\) columns (features). The churn column is our target which indicate whether customer churned (left the company) or not.
data(churn_tel)the churn_tel dataset, as a data frame, contains \(7043\) rows (customers) and \(21\) columns (variables/features). the \(21\) variables are:
customer.ID: Customer ID.
gender: Whether the customer is a male or a female.
senior.citizen: Whether the customer is a senior citizen or not (1, 0).
partner: Whether the customer has a partner or not (yes, no).
dependent: Whether the customer has dependents or not (yes, no).
tenure: Number of months the customer has stayed with the company.
phone.service: Whether the customer has a phone service or not (yes, no).
multiple.lines: Whether the customer has multiple lines or not (yes, no, no phone service).
internet.service: Customer's internet service provider (DSL, fiber optic, no).
online.security: Whether the customer has online security or not (yes, no, no internet service).
online.backup: Whether the customer has online backup or not (yes, no, no internet service).
device.protection: Whether the customer has device protection or not (yes, no, no internet service).
tech.support: Whether the customer has tech support or not (yes, no, no internet service).
streaming.TV: Whether the customer has streaming TV or not (yes, no, no internet service).
streaming.movie: Whether the customer has streaming movies or not (yes, no, no internet service).
contract: the contract term of the customer (month to month, 1 year, 2 year).
paperless.bill: Whether the customer has paperless billing or not (yes, no).
payment.method: the customer's payment method (electronic check, mail check, bank transfer, credit card).
monthly.charge: the amount charged to the customer monthly.
total.charges: the total amount charged to the customer.
churn: Whether the customer churned or not (yes or no).
For more information related to the dataset see:
https://www.kaggle.com/blastchar/telco-customer-churn
Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.
bank,
churn_mlc,
churn,
adult,
risk,
cereal,
advertising,
marketing,
drug,
house,
house_price,
red_wines,
white_wines,
insurance,
caravan,
fertilizer,
corona
data(churn_tel)
str(churn_tel)
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