Statistically Solving Sneezes and Sniffles – a Work in Progress Report at PyDataLondon 2016

This is a Work in Progress report, presented this morning at my PyDataLondon 2016 conference. A group of 4 of us are modelling a year’s worth of self-reported data from my wife around her allergies – we’re learning to model which environmental conditions cause her sneezes such that she might have more control over her antihistamine use. Join the email updates list for low-volume updates about this project.

I really should have warned my audience that I was about to photograph them (honest – they seemed to enjoy the talk!):

Emily created the Allergy Tracker (open src) iPhone app a year ago, she logs every sneeze, antihistamine, alcoholic drink, runny nose and more. She’s sneezed for 20 years and by heck, we wondered if we could apply some Data Science to the problem to see if her symptoms correlate with weather, food and pollution. I’m pleased to say we’ve made some progress – it looks like humidity is connected to her propensity to use an antihistamine.

This talk (co-presented with Giles Weaver) discusses the data, the app, our approach to analysis and our tools (including Jupyter, scikit-learn, R, Anaconda and Seaborn) to build a variety of machine learned models to try to model antihistamine usage against external factors. Here are the slides:

Now we’re moving forward to a couple of other participants (we’d like a few more to join us – if you’re on iOS and in London and can commit to 3 months consistent usage we’ll try to tell you what drives your sneezes). We also have academic introductions so we can validate our ideas (and/or kick them into the ground and try again!).

This is the second full day of the conference – we have 330 attendees and we’ve had 2 great keynote speakers and a host of wonderful talks and tutorials (yesterday). Tonight we have our conference party. I’m super happy with how things are progressing – many thanks to all of our speakers, volunteers, Bloomberg and our sponsors for making this work so well.

Update – featured in Mode Analytics #23.

Update – I did a follow-up talk at ODSC 2016 with notes on a new medication that we’ve tried.

Ian is a Chief Interim Data Scientist via his Mor Consulting. Sign-up for Data Science tutorials in London and to hear about his data science thoughts and jobs. He lives in London, is walked by his high energy Springer Spaniel and is a consumer of fine coffees.