The genesis of Daniel’s commitment to data-driven decision making was in his previous career as an online poker professional. Staked by Team Moshman, a top online poker backer, he mined data from his opponents and visually displayed statistics relating to their playing tendencies. Focusing on No-Limit Texas Hold’em, he played as many as 800 hands per hour, while living on the beach in Baja California, Mexico. Now, back in the real world, he helps companies leverage their data for strategic planning and decision-making with SharpData, the consulting company he started. As a native of Southern California, he enjoys surfing, golfing, and playing guitar in his spare time. He completed his undergraduate work with a Bachelor of Arts degree from UCLA.
Scikit-Learn Tutorial: Baseball Analytics in Python Pt 2
A Scikit-Learn tutorial to using logistic regression and random forest models to predict which baseball players will be voted into the Hal…