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Category: Data Science

Building an F1 prediction engine – Predictive Modelling Part I ?>

Building an F1 prediction engine – Predictive Modelling Part I

In the previous series of posts I discussed and explained the steps involved in Feature Engineering. In this series, I will talk about the coolest part of applied ML; the Predictive Modelling phase. This is where you get to use all the ‘magic’ power of machine learning algorithms and see the performance of any models you build. In this post I’ll start by showing the most common evaluation metrics and then reveal the custom evaluation metric I use for assessing…

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

Building an F1 prediction engine – Feature Engineering Part V

Up till now we have discussed on how we can create some features and how we can encode them. We’ve, hopefully, ended up with lots of variables and we are seeking a way to keep the top-performing ones. In this post I’m going to explain ‘why’ and ‘how’ we should cleverly select our features. Please share the post:

Model Interpretability with SHAP ?>

Model Interpretability with SHAP

In applied machine learning, there is usually a trade-off between model accuracy and interpretability. Some models like linear regression or decision trees are considered interpretable whereas others, such as tree ensembles or neural networks, are used as black-box algorithms. While this is partly true, there have been great advances in the model interpretability front in the past few months. In this post I will explain one I the newest methods in the field, the so-called SHAP, and show some practical…

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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: