/Churn-Analysis-Using-Random-Forest-and-Logit

Implementation of Churn Analysis of a Telecomm company using Logistic Regression and Random Forest

Primary LanguageR

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".