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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:

Building an F1 prediction engine – Problem Definition ?>

Building an F1 prediction engine – Problem Definition

In this series of posts I’m going to explain the process behind building a Machine-Learning model capable of predicting F1 race outcomes. This is not intented to be a complete guide so some background on building ML pipelines is assumed. Please share the post: