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The reconstruction of neutrino events in the IceCube experiment is crucialfor many scientific analyses, including searches for cosmic neutrino sources.The Kaggle competition "IceCube -- Neutrinos in Deep ice" was a public machinelearning challenge designed to encourage the development of innovativesolutions to improve the accuracy and efficiency of neutrino eventreconstruction. Participants worked with a dataset of simulated neutrino eventsand were tasked with creating a suitable model to predict the direction vectorof incoming neutrinos. From January to April 2023, hundreds of teams competedfor a total of $50k prize money, which was awarded to the best performing fewout of the many thousand submissions. In this contribution I will present someinsights into the organization of this large outreach project, and summarizesome of the main findings, results and takeaways.

Public Kaggle Competition "IceCube -- Neutrinos in Deep Ice"