8.2 Practical Machine Learning with R Churn Analysis (balanced with SMOTE)

Practical Machine Learning with R (Chapter 8)

Churn Analysis (unbalanced with SMOTE)

This workflow repeats the Churn Analysis from the textbook Practical Machine Learning with R (https:\\ai.lange-analytics.com). We are using unbalanced data for a churn analysis. "Unbalanced" means that one class (customers who did not churn) contains significantly more observations than the other class (customers who churned). Check the Value Count Node (Churn Count Before SMOTE). Here, we use SMOTE to balance the data (see Value Count Node (Churn Count After SMOTE). Check the Sensitivity and Specificity in the Scorer to see how much better the model performs compared to the "unbalanced data" model.

External resources

• Contact the author
• A workflow for the "unbalanced data" model is in this space
• Here you can open the R analysis with SMOTE in RStudio
• ai.lange-analytics.com