Tonight is the opening night of the 2014-15 NBA season. This year, we will be running a machine learning algorithm aimed at estimating underlying features characterizing each team. With these features, we hope to identify interesting match-ups (including potential upsets), similar team-playing-style categories, and win-loss probabilities for future games. As of now, the only source data that we intend to feed our system will be win-loss results of completed games. As the season progresses, our algorithm will thus have more and more data informing it — It will be interesting to see if it can begin to provide accurate predictions by the end of the season. Stay tuned for periodic updates on this experiment!
In our first set of posts here, we explore the possibility of using historical traffic data to train a machine learning algorithm capable of predicting near-term highway conditions — say, up to an hour into the future, at any given time. To try our hand at this, we will be working with publicly available data provided by the California Performance Measurement System (PEMS). (more…)