Which machine learning technique is MOST effective for retail churn prediction when dealing with highly imbalanced datasets where churned customers represent a small minority?

In the intersection of retail science and data analytics, predictive modeling has transformed how businesses understand customer churn. Modern retail organizations deploy sophisticated algorithms to identify at-risk customers before they leave. Test your knowledge about how scientific methods are revolutionizing retail churn prediction and the underlying technologies that make it possible.

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Simple linear regression with no data adjustments
Ensemble methods with SMOTE (Synthetic Minority Over-sampling Technique)
K-means clustering without feature normalization
Basic decision trees without pruning or balancing techniques