Logistic regression is a statistical technique for classifying records based on values of input fields. It is analogous to linear regression, but takes a categorical target field instead of a numeric one.

For example, suppose a telecommunications provider is concerned about the number of customers it's losing to competitors. If service usage data can be used to predict which customers are liable to transfer to another provider, offers can be customized to retain as many customers as possible.

This example uses the flow named Telecommunications Churn, available in the example project . The data file is telco.csv.

This example focuses on using usage data to predict customer loss (churn). Because the target has two distinct categories, a binomial model is used. In the case of a target with multiple categories, a multinomial model could be created instead. See Classifying telecommunications customers for more information.