In this article, we’ll do a simple sales forecast model and then blend external variables (properly done). So we’ll use the same model and we won’t do data wrangling or engineering at any point, so that we can tell apart only the benefit of adding useful features.
“To better understand the marketplace, it is incumbent for organizations to look beyond their own four walls for data sources.”
Douglas Laney (VP, Gartner Research)
There have been several implementations of the popular Walmart Sales Forecast competition to predict their sales.
Screenshot from the Kaggle Competition
However, all of them seem to attempt to increase accuracy *(reduce error)by focusing on mainly *two things:
1) Feature engineering (getting the most out of your features)
2) Model/parameter optimization (choosing best model & best parameters)
Both of the above are very necessary indeed, but there is a third thing that *adds value *in a complementaryway, and it’s wildly underused not only in this use case (which understandably was against the rules of the competition) but in most data science projects:
In this article, we’ll do a simple sales forecast model and then blend external variables (properly done).
The title of this article refers to improving all models, not because of doing something else, but by doing the same thing with more useful data.
So we’ll use the same model and we won’t do data wrangling *or engineering at any point, so that we can tell apart only the benefit of *adding useful features.
Step by step tutorial on making a powerful Demand Forecast with Python. There have been several implementations of the popular Walmart Sales Forecast competition to predict their sales.
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