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Category: Feature Engineering

Building an F1 prediction engine – Feature Engineering Part III ?>

Building an F1 prediction engine – Feature Engineering Part III

After creating all features, it is quickly evident that there are a lot of missing data. Missing data should be dealt with before going on to the next phases of our model building. In this post, I’m going to quickly describe the reasons behind the missing values and ways to treat them. Please share the post:

Building an F1 prediction engine – Feature Engineering Part II ?>

Building an F1 prediction engine – Feature Engineering Part II

Having explored the data, checked what is available and what’s not, found any inconsistencies and potential problems, it is time to get creative. Feature extraction is the step where an experienced data scientist can really make the difference and improve the subsequent model’s accuracy; many times more than it will be possible through algorithm selection and fine-tuning. Please share the post: