add this missing content: Use Case 2 Customer churn prediction (telecom / subscription services). Problem / opportunity: Companies want to predict which customers are likely to cancel service so they can intervene (offers, retention campaigns). This is a binary classification task (churn / stay). How a decision tree solves it: A decision tree uses customer features (tenure, usage, billing issues, customer-service calls, plan type) to split customers into segments with different churn probabilities. For example, a branch might identify long-tenure but low-usage customers with recent billing complaints as high churn risk. The model's rule structure helps marketing craft targeted retention messages and identify high-value at-risk segments. Because decision trees handle categorical and numerical inputs and produce transparent rules, they are practical for business stakeholders who need interpretable strategies (Gentek et al., 2022; Breiman et al., 1984). Implementation notes and considerations. Practically, one would (1) assemble labeled historical data, (2) preprocess missing values and encode categories, (3) train a DecisionTreeClassifier (or an ensemble) with cross-validation, (4) prune or constrain depth to avoid overfitting, and (5) evaluate with precision/recall or ROC depending on class imbalance (scikit-learn, 2024). For sensitive domains (healthcare, finance), pair predictive models with human review and monitor model drift.Conclusion. Decision trees are well suited f