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This canvas builds a comprehensive machine learning pipeline to predict long-term user success by analyzing behavioral signals across 4,775 users and 409K events, using Gradient Boosting and Random Forest models that achieve near-perfect AUC of 1.0 to identify the five key predictive behaviors (total events, session duration, active days, feature adoption, and activity intensity) and generating actionable product recommendations ranked by impact-to-effort ratio.