Using the cleaned retail data, you must build a model to predict whether a customer will place an order next month.

Split the prepared dataset into 80/20 train–test sets with stratification on the target variable. Standardize numeric features and one-hot encode categorical features in a reproducible pipeline. Train a gradient-boosted tree (e.g., XGBoost or LightGBM) and report AUC on the held-out test set. List two techniques you would use to improve the model’s generalization if AUC is low.

Demonstrate scikit-learn pipelines and proper evaluation.

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