This dataset originates from the MLC++ machine learning software and is used for modeling customer churn. Customer churn, also known as customer attrition, refers to the event in which customers stop doing business with a company. The dataset contains \(5000\) rows (customers) and \(20\) columns (features). The churn column serves as the target variable, indicating whether a customer has churned (left the company) or not.
data(churn)A data frame with \(5000\) rows (customers) and \(20\) columns (variables/features). the \(20\) variables are:
state: Categorical, for the \(51\) states and the District of Columbia.
area.code: Categorical.
account.length: count, how long account has been active.
voice.plan: Categorical, yes or no, voice mail plan.
voice.messages: Count, number of voice mail messages.
intl.plan: Categorical, yes or no, international plan.
intl.mins: Continuous, minutes customer used service to make international calls.
intl.calls: Count, total number of international calls.
intl.charge: Continuous, total international charge.
day.mins: Continuous, minutes customer used service during the day.
day.calls: Count, total number of calls during the day.
day.charge: Continuous, total charge during the day.
eve.mins: Continuous, minutes customer used service during the evening.
eve.calls: Count, total number of calls during the evening.
eve.charge: Continuous, total charge during the evening.
night.mins: Continuous, minutes customer used service during the night.
night.calls: Count, total number of calls during the night.
night.charge: Continuous, total charge during the night.
customer.calls: Count, number of calls to customer service.
churn: Categorical, yes or no. Indicator of whether the customer has left the company (yes or no).
For more information related to the dataset see
- OpenML: https://openml.org/search?type=data&sort=runs&id=40701&status=active
- data.world: https://data.world/earino/churn
Saha, S., Saha, C., Haque, M. M., Alam, M. G. R., and Talukder, A. (2024). ChurnNet: Deep learning enhanced customer churn prediction in telecommunication industry. IEEE access, 12, 4471-4484.
Umayaparvathi, V., and Iyakutti, K. (2016). A survey on customer churn prediction in telecom industry: Datasets, methods and metrics. International Research Journal of Engineering and Technology (IRJET), 3(04), 1065-1070
Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.
bank,
churnCredit,
churnTel,
adult,
risk,
cereal,
advertising,
marketing,
drug,
house,
housePrice,
redWines,
whiteWines,
insurance,
caravan,
fertilizer,
corona