Which mathematical model is most commonly used as the foundation for retail customer churn prediction when dealing with binary outcomes (churn/no churn)?

Predictive analytics in retail relies heavily on sophisticated mathematical models to identify customers at risk of churning. These models transform customer behavior data into actionable insights, allowing retailers to implement targeted retention strategies. Test your knowledge of the mathematical foundations that power modern retail churn prediction systems and their practical applications in the competitive retail landscape.

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Logistic Regression with L1/L2 regularization
Simple Linear Regression with mean squared error
K-means Clustering with Euclidean distance
Principal Component Analysis (PCA) with SVD