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Repository for Jupyer Notebook comparing performances of GLM implementations in languages and libraries

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Predicting Airline Data using a Generalized Linear Model (GLM)

In this repository, we collect Jupyer Notebooks running simple GLM data analysis in view of comparing performances of GLM implementations across languages and libraries

We use the year 2008 airline dataset taken on http://stat-computing.org as a benchmark example. We predict the probability that a flight is late based on its departure date/time, the expected flight time and distance, the origin and destitation airports.

Considerations

The objective of these notebooks are only to define a simple model offerring a point of comparison in terms of computing performances across datascience language and libraries. In otherwords, this notebooks are not for you if you are looking for the most accurate model in airline predictions.

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