MLNE - Bike sharing demand dojo (Regression Session pt.2) October 29th 2015 @campusnorthuk
Thomas Merritt-Smith
This month, following on from an introduction to regression last time, we’ll get our hands dirty and do some proper coding. We’ll be tackling a problem hosted by Kaggle, the Machine Learning competition site.
Big thanks as usual to @Campusnorthuk for hosting. Pizzas and beer. What more do you want!
Campus North October 29th, 6.30pm.
Sign up here: http://attending.io/events/mlne-bike-sharing-dojo
Check it out here: https://www.kaggle.com/c/bike-sharing-demand
Here’s the blurb from kaggle:
Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.
The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city. In this competition, participants are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.