Having defined what we are trying to achieve, it’s time to start thinking about what data we need and how we are going to obtain them in order to create features and train our models.
Hereby I present you with the first predictions made by the algorithm. The model has not seen any data from the recent testing in Barcelona so everything is based on the drivers’ and teams’ performance in the past years. I expect the accuracy of the predictions to increase as the season progresses. Race predictions will be updated after the qualifying.
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.
Welcome to F1 predictor, a blog in the crossroads of my two passions: Formula 1 and Data Science. If you are passionate about any of the above, I’m sure you’ll find something interesting in here!