Michael Pollinger

Michael Pollinger is a Data Science professional with a master's degree in economics from the University of California Davis. His areas of interest span across applied game and auction theory in digital marketing, machine learning models and time series econometrics in finance, and the development of improved metrics for school quality and performance. With a diverse portfolio, Michael has engaged in various projects including time series analysis on historical home price appreciation trends, forecasting NBA player performance using lasso linear regression, conducting monte-carlo simulations of RTB auctions, building an NLP-based wine recommender system, and clustering California high schools based on admission data. His expertise includes Python, Machine Learning, Time-Series Data, Forecasting, Web Scraping, Text Analysis, Econometrics, SQL, and Tableau. Michael's educational background includes a Master's degree in Economics from UC Davis, where he also completed his Bachelor's degree in Economics.