Eugene Yan • 6/20/2015

DataScience SG Meetup - How we got top 3% in Kaggle

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The article details a presentation given at a DataScience SG meetup, where the author shared their approach to placing 85th out of 3514 in Kaggle's Otto competition. It covers key technical aspects like the evaluation metric, validation strategies, feature engineering, transformation methods, machine learning models, and ensembling techniques used to achieve a top-ranking result.

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#Machine Learning #Data Science #Kaggle
DataScience SG Meetup - How we got top 3% in Kaggle

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