# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
###### Abstract  
Flight delays represent a significant challenge in the global aviation industry, resulting in substantial costs and a decline in passenger satisfaction. This study addresses the critical issue of predicting flight delays exceeding 15 min using machine learning techniques. The arrival delays at a Turkish airport are analyzed utilizing a novel dataset derived from airport operations. This research examines a range of machine learning models, including Logistic Regression, Naive Bayes, Neural Networks, Random Forest, XGBoost, CatBoost, and LightGBM. To address the issue of imbalanced data, additional experiments are conducted using the Synthetic Minority Over-Sampling Technique (SMOTE), in conjunction with the incorporation of meteorological data. This multi-faceted approach ensures robust forecast performance under varying conditions. The SHAP (SHapley Additive exPlanations) method is employed to interpret the relative importance of features within the models. The study is based on a three-year period of flight data obtained from a Turkish airport. The dataset is sufficiently extensive and robust to provide a reliable foundation for analysis. The results indicate that XGBoost is the most proficient model for the dataset, demonstrating its potential to deliver highly accurate predictions with an accuracy of 80%. The impact of weather factors on the predictions is found to be insignificant in comparison to scenarios without weather data in this dataset.  
flight delay prediction; forecasting; machine learning; performance analysis +
Footnote †: journal: Medical  
Airlines are also affected by delays in terms of their operating costs. The occurrence of persistent delays on specific routes has the potential to erode an airline's appeal to customers over time [8]. Even airports are not immune to the negative effects of delays, as passenger satisfaction levels decline significantly when such occurrences occur [5; 9]. Another crucial aspect to be considered is the environmental impact of flight delays. It was estimated that U.S. airlines expend a considerable 2 billion liters of jet fuel annually as aircraft remain idling on runways due to flight delays [10]. This heightened fuel consumption not only results in economic losses but also amplifies carbon emissions, thereby exacerbating environmental concerns [11]. In light of these considerations, it is evident that flight delays have a multitude of adverse effects on stakeholders, encompassing both financial costs and other detrimental consequences.  
To mitigate the adverse effects of flight delays, it is of the utmost importance to be able to accurately predict when such delays are likely to occur. The objective of this study is to predict whether flights will be delayed by more than 15 min, utilizing readily available weather and airport data. The primary objective of this study is to analyze the arrival performance of the selected airport. It is evident that arrival delays not only affect the on-board passengers but also have an impact on future flight plans utilizing the same aircraft. Given the uniqueness of the dataset in comparison to those utilized in the existing literature, a variety of machine learning techniques, including Logistic Regression, Naive Bayes, Neural Networks, XGBoost, Random Forests, CatBoost, and LightGBM, are explored to determine the most suitable approach.  
This paper proceeds with a detailed examination of the research stages. The research study commences with the explicit definition of the research question, which is followed by a comprehensive review of the existing literature. Subsequently, the methods employed are elucidated, with detailed information provided about the dataset. Subsequently, the findings are presented and discussed in depth, leading to recommendations for future research.