Which machine learning technique has proven most effective for predicting customer churn in retail when dealing with imbalanced datasets (where churners are significantly fewer than non-churners)?

Customer churn is one of the most challenging issues for retailers today, costing billions in lost revenue annually. The science behind predicting and preventing churn has evolved dramatically with advanced analytics and AI. Test your knowledge about modern retail churn prediction techniques that are transforming how businesses retain their valuable customers.

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Simple Logistic Regression with no modifications
Ensemble methods combined with SMOTE (Synthetic Minority Over-sampling Technique)
Basic k-means clustering without feature engineering
Standard Neural Networks without class weighting or resampling