Customer Churn Data

Description

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.

Usage

data(churn)

Value

Source

http://www.sgi.com/tech/mlc/, http://www.rulequest.com/see5-examples.html

Results