Pinned Repositories
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Mining-the-Social-Web-3rd-Edition
The official online compendium for Mining the Social Web, 3rd Edition (O'Reilly, 2018)
predictably_divided
Achieved 96% accuracy in predicting a person's vote in the 2020 presidential election based on answers to survey questions. Determined that the Affordable Care Act, the proposed border wall, and Trump's first impeachment were among the strongest predictors.
race_louisville_traffic_stops
In analyzing more than 100,000 traffic stops in Louisville, Kentucky, determined that the probability of a motorist being searched depended on the race of the motorist, race of the police officer, and location of the stop.
stop_the_steal_networks
Used network analysis and topic modeling to examine the content around the #StopTheSteal hashtag in tweets sent shortly before and after the storming of the U.S. Capitol.
henryhoenig's Repositories
henryhoenig/handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
henryhoenig/Mining-the-Social-Web-3rd-Edition
The official online compendium for Mining the Social Web, 3rd Edition (O'Reilly, 2018)
henryhoenig/predictably_divided
Achieved 96% accuracy in predicting a person's vote in the 2020 presidential election based on answers to survey questions. Determined that the Affordable Care Act, the proposed border wall, and Trump's first impeachment were among the strongest predictors.
henryhoenig/race_louisville_traffic_stops
In analyzing more than 100,000 traffic stops in Louisville, Kentucky, determined that the probability of a motorist being searched depended on the race of the motorist, race of the police officer, and location of the stop.
henryhoenig/stop_the_steal_networks
Used network analysis and topic modeling to examine the content around the #StopTheSteal hashtag in tweets sent shortly before and after the storming of the U.S. Capitol.