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Category: Data Science

2017 season overview ?>

2017 season overview

2017 season is, unfortunately, over and at the same time that was the end of the first season F1-predictor published qualifying and race predictions. In this post I would like to describe my experience from this first year of keeping this blog and – most importantly – provide an objective assessment of 2017 predictions! Please share the post:

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:

Announcing F1-Predictor API ?>

Announcing F1-Predictor API

Since I started this blog, I’m always wondering how I can improve it, what should I write about and how to apply ‘data science’ on anything F1-related. Today, I’m happy to announce that the qualifying and race predictions plus some additional insights (see further down for details) will be exposed via an API and they will be available in JSON format. 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:

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: