Forecasting elections with Bayesian inference

Dashboards for electoral forecasting models in PyMC3.

This repository is a collection of dashboards displaying the results of electoral forecasting models implemented in PyMC3. For now, the models are focused on forecasting French elections.

What is it?

The models are open-sourced and stand on the shoulders of giants of the Python data stack: PyMC3 for state-of-the-art MCMC algorithms, ArviZ and Bokeh for visualizations, and Pandas for data cleaning.

More details about the project as well as tutorials detailing how the models work are available here.

We warmly thank all the developers who give their time to develop these free, open-source and high quality scientific tools – just like The Avengers, they really are true heroes.

Who is it?

This project is maintained and spearheaded by Alexandre Andorra and Rémi Louf.

By day, I’m a Bayesian modeler at the PyMC Labs consultancy and host the most popular podcast dedicated to Bayesian inference out there – aka Learning Bayesian Statistics.

By night, I don’t (yet) fight crime, but I’m an open-source enthusiast and core contributor to the awesome Python packages PyMC and ArviZ.

An always-learning statistician, I love building models and studying elections and human behavior. I also love Nutella a bit too much, but I don’t like talking about it – I prefer eating it 😋

Feel free to reach out on Twitter if you want to talk about chocolate, statistical modeling under certainty, or how “polls are useless now because they missed two elections in a row!” – yeah, I’m a bit sarcastic.

On that note, go forth and PyMCheers :vulcan_salute: