Global Stability & Risk Forecasting (GDELT)
Analyzed subsets of a 2PB global events dataset to identify risk trends and support stability forecasting, including anomaly detection and a validated Random Forest model.
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Analyzed subsets of a 2PB global events dataset to identify risk trends and support stability forecasting, including anomaly detection and a validated Random Forest model.
Built end-to-end ETL pipelines with PySpark/Spark SQL to ingest 23GB of logs into a Data Lake using Medallion Architecture, then delivered KPI dashboards in Tableau/Power BI.
Built a Top-N recommender using implicit-feedback ALS on the Steam-200k dataset, learning player preference profiles from gameplay behavior and recommending similar games.