Sunday 13:30–15:45 in Mortimer Room

Politics Hackathon

John Sandall, Frank Kelly

Audience level:


Do you like Nate Silver's FiveThirtyEight datablog? Ever dreamed of becoming a psephologist? Come along to this political data hackathon and try your hand at forecasting a general election! There's a myriad of interesting datasets to look at, some extremely complex geospatial and time series problems to solve, and a world of "open" data that isn't nearly open enough. Run by @SixFiftyData.


When Theresa May announced plans in April 2017 for the UK to hold a general election, the public may have despaired but we formed SixFifty - a collaboration of data scientists, software engineers, data journalists and political operatives. We wanted to understand why forecasting elections in the UK using open data is notoriously difficult, and to see how far good statistical practice and modern machine learning methods could take us. We also wanted to make political and demographic data more open and accessible by showcasing and releasing the cleaned versions of the datasets we're using.

Now we invite you to join us in digging into all the datasets we came across, data from previous elections, polling data, data from multiple censi, and see if you can build an even better election prediction model. Or perhaps you'd like to tackle some of the hardest challenges we faced, such as turning PDF polling tables into usable information (for an example, see

Prize categories


You don’t have to use these, but they’re a good start.

Intellectual Property

All Hackathon submissions remain the intellectual property of the individuals or organisations that developed them. We encourage participants to open source their projects to both share their hacks with the greater community and promote innovation in this space.

¹ ML competition conditions: Models must use prior data only (the results are a matter of historical record!). Code must be made openly available for review via GitHub. With the winner’s permission, SixFifty will work with competition winners to publish the winning models with commentary on

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