This competition is about predicting whether a customer will change telecommunications provider, something known as "churning".
The training dataset contains 4250 samples. Each sample contains 19 features and 1 boolean variable "churn" which indicates the class of the sample.
The 19 input features and 1 target variable are:
"state",
string
. 2-letter code of the US state of customer residence
"account_length",
numerical
. Number of months the customer has been with the current telco provider
"area_code",
string
="area_code_AAA" where AAA = 3 digit area code.
"international_plan",
(yes/no)
. The customer has international plan.
"voice_mail_plan",
(yes/no)
. The customer has voice mail plan.
"number_vmail_messages",
numerical
. Number of voice-mail messages.
"total_day_minutes",
numerical
. Total minutes of day calls.
"total_day_calls",
numerical
. Total minutes of day calls.
"total_day_charge",
numerical
. Total charge of day calls.
"total_eve_minutes",
numerical
. Total minutes of evening calls.
"total_eve_calls",
numerical
. Total number of evening calls.
"total_eve_charge",
numerical
. Total charge of evening calls.
"total_night_minutes",
numerical
. Total minutes of night calls.
"total_night_calls",
numerical
. Total number of night calls.
"total_night_charge",
numerical
. Total charge of night calls.
"total_intl_minutes",
numerical
. Total minutes of international calls.
"total_intl_calls",
numerical
. Total number of international calls.
"total_intl_charge",
numerical
. Total charge of international calls
"number_customer_service_calls",
numerical
. Number of calls to customer service
"churn",
(yes/no)
. Customer churn - target variable.
The submissions will be evaluated using the test Accuracy criterion:
\[
\text{Accuracy} = \frac{\text{Number of correct predictions}}{\text{Number of total test samples}}
\]