In this dataset, we are looking at Formula One, the highest class of international racing for open-wheel single-seater formula racing. It consists of 10 different teams with 20 drivers. Points are awarded to top 10 finishers in a Grand Prix, additional point is awarded to driver with the fastest lap, but driver have to finish in the top 10 in order to qualify for that point. In F1, to stand on the podium, driver must finish top 3 in a Grand Prix, standing on a podium is a wish for most drivers as they will be given media attention to. They are also representation of the best performing driver for that Grand Prix. In addition, drivers finishing in the top 3 position also earn more points as compared to drivers that finish later. As audience or constructors (team), we are interested who will be on the podium for each race, as audience are interested in it. As constructors, should their driver finished in the top 3 position, the constructor will be recognised which helps them to gain popularity and increase the chances of incoming advertisers, bringing revenue for them. Some workers’ contracts stated bonus, should their driver finish in top 3 or wins the race. Hence, how can audience or constructor know if a driver is going to finish in top 3 position?
There are 13 files / tables storing information about Formula One (F1) and a detailed description and information for the 13 tables in "datasets". There are two machine learning models built, Naive Baseline Model and Logistic Regression Model and evaluating the performance of the models by its accuracy score in percentages (%). In which a higher accuracy score suggest that the model predicts more better/ more accurately, a machine learning model is considered performing well when its accuracy score is higher than the Naive Baseline Model.