About

This canvas performs a comprehensive analysis of Zerve user behavior to predict engagement and success through a "semantic density" metric that combines tool diversity, workflow coherence, and credit efficiency. The workflow chains five Python blocks—loading and profiling the dataset, categorizing 141 event types, engineering 40+ user-level behavioral features (including a novel semantic density score), extracting top-user signatures, and building a prompt optimizer that scores prompts for clarity and predicts credit savings—ultimately generating a production-ready 5-tab interactive Streamlit dashboard displaying user cohorts, workflow progression funnels, credit ROI, individual journey exploration, and live prompt optimization with linguistic feature profiling.