A data set from the MLC++ machine learning software for modeling
customer churn. There are 19 predictors, mostly numeric: state
(categorical), account_length, area_code,
international_plan (yes/no), voice_mail_plan (yes/no),
number_vmail_messages, total_day_minutes,
total_day_calls, total_day_charge,
total_eve_minutes, total_eve_calls,
total_eve_charge, total_night_minutes,
total_night_calls, total_night_charge,
total_intl_minutes, total_intl_calls,
total_intl_charge and number_customer_service_calls.
The outcome is contained in a column called churn (also yes/no).
The training data has 3333 samples and the test set contains 1667.
A note in one of the source files states that the data are "artificial based on claims similar to real world".
A rule-based model shown on the RuleQuest website contains 19 rules, including:
Rule 1: (2221/60, lift 1.1)
international plan = no
total day minutes <= 223.2
number customer service calls <= 3
-> class 0 [0.973]Rule 5: (1972/87, lift 1.1)
total day minutes <= 264.4
total intl minutes <= 13.1
total intl calls > 2
number customer service calls <= 3
-> class 0 [0.955]
Rule 10: (60, lift 6.8)
international plan = yes
total intl calls <= 2
-> class 1 [0.984]
Rule 12: (32, lift 6.7)
total day minutes <= 120.5
number customer service calls > 3
-> class 1 [0.971]
This implementation of C5.0 contains the same rules, but the rule numbers are different than above.
data(churn)The training set
The test set.