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This canvas implements a comprehensive user success prediction and behavioral analytics pipeline that ingests event data, builds user profiles, constructs a directed behavior graph, tests 12 causal hypotheses about retention/monetization, and trains a HistGradientBoosting ML model to predict user success with >0.75 AUC using permutation-based feature importance.