Abstract
<jats:p>Formula 1 racing is a data-driven sport, where applying data science techniques can provide competitive edge. This chapter explores machine learning models to predict driver positions on a given racetrack. Utilizing machine learning models like Random Forest, Gradient Boosting, Support Vector Classifier, K-Nearest Neighbors, Gaussian Naive Bayes, Decision Trees, and Multi-layer Perceptron, we have explored their efficacy in forecasting race outcomes. The analysis is based on historical data encompassing of driver skills, team performance, weather conditions, and track characteristics. The goal is to provide insights that can help teams to optimize their race strategies, identify areas for improvement, and gain an advantage over competitors. The analysis revealed that Gradient Boosting and Decision Trees emerged as the top-performing models, showcasing their ability to capture complex patterns within the data. Additionally, The methodologies defined can potentially be applied to other motorsports as well.</jats:p>