Propensity model training with XGBoost
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Updated
Sep 10, 2024 - Python
Propensity model training with XGBoost
ML-powered channel optimization engine for pharma sales reps. Engagement-based NBA with XGBoost propensity models, Streamlit dashboard, and synthetic data for the German pharma market.
This project segments Starbucks customers using transaction and offer data. Through preprocessing, feature engineering, and clustering (K-Means), it identifies distinct customer groups, providing insights to personalize marketing, improve engagement, and boost customer retention.
End-to-end e-commerce product analytics platform with BigQuery SQL, Tableau dashboards, purchase propensity modeling, A/B testing, and an AI analytics copilot.
A self-hosted BigQuery ML pipeline that predicts purchase propensity from GA4 events and pushes the result back to GA4 as a user property via the Measurement Protocol. Built for Google Ads remarketing. Consent-aware, cost-capped, and production-hardened.
Propensity scoring model for user conversion prediction
Predicts which dormant B2B clients will reactivate if contacted. End-to-end ML pipeline turns 2M transactions into a campaign-ready priority list via Extra Trees + RFM feature engineering. Delivers 3.4× lift over random outreach. Python, scikit-learn, XGBoost.
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