The workflow begins by loading the telecom churn dataset using the Dataset Structured node. Categorical variables are indexed using the String Indexer, and the Vector Assembler combines all features into a single vector for modeling.

The dataset is split into training and testing subsets with the Split node. The Decision Tree Classifier trains a predictive model to identify customers likely to churn. Predictions are generated using the Predict node, and the results are displayed with Print N Rows for analysis.

This workflow provides an interpretable machine learning approach for understanding churn behavior and improving customer retention strategies.