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User behavior analysis project using Zerve application event data (409k+ events).
Event logs were aggregated into user-level features including total activity, notebook execution, sign-ins, and agent tool usage.

Successful users were defined as those triggering the credits_used event.

A logistic regression model was trained to predict successful usage based on engagement patterns.
Results show strong correlation between workflow depth, repeated interaction, and long-term platform success.