Şeker, FerhatBozkurt, Alper2025-01-062025-01-0620242147-877510.21325/jotags.2024.1402https://doi.org/10.21325/jotags.2024.1402https://search.trdizin.gov.tr/tr/yayin/detay/1230388https://hdl.handle.net/20.500.14669/500Based on the probability theory, which is used to model uncertainty and randomness in real-world situations, the study aims to understand the impact of uncertain conditions, such as the Covid-19 pandemic, on the accuracy of the algorithms and the resulting losses to a country's tourism industry. The contribution of this paper to the international body of knowledge is twofold: firstly, it advances theoretical understanding of the use of probability theory in modelling real-world problems; and secondly, it offers a methodological approach for estimating tourist arrivals that accounts for the impact of extreme events. To achieve these aims, the Levenberg-Marquardt optimization was first applied to determine the optimal coefficients of the exponential function for estimating tourist arrivals from 1950 to 2020. Next, the K-Star machine learning algorithm was applied to the dataset with and without Covid-19 cases to estimate tourist arrivals.eninfo:eu-repo/semantics/openAccessMachine learningArtificial intelligenceLevenberg-Marquardt OptimizationK* algorithmArticle7171703123038812