About

This canvas performs end-to-end predictive analytics on Zerve user behavior to identify free-to-paid upgrade drivers and segment users into four distinct archetypes using machine learning. The workflow loads hackathon event data, engineers 50+ behavioral features from user activity in the first 14 days, trains a Gradient Boosting classifier (achieving 0.76 AUC) to predict upgrades, applies K-Means clustering to create interpretable user archetypes (Power Users, Deploy-Focused, Casual Explorers, Dormant), and generates comprehensive SHAP-based explanations with publication-ready visualizations showing that deployment actions and engagement depth are the strongest predictors of monetization.