![]() The complete notebook with the code for this article is available on GitHub here. The main events of rain that year caused a significantly low number of rides. The below plot is showing the NYC Taxi rides data in blue and the rain levels in orange. We will try to add weather data to two forecasting data from two Kaggle competitions in the article. However, they are often better than trying to completely automate the building of the machine learning models (“Auto-ML”), both for accuracy and trust reasons. Not all the hypotheses will prove helpful. It can improve the accuracy of the models and the trust of human users in the forecasts that the models generate. It is critical to get the human experts' anecdotes from their experience into the machine learning models. The article's focus is to make it quickly and easily as a template for trying to add external factors to machine learning models.
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