# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
## 2 Problem Definition  
Air travel plays a pivotal role in modern society, offering unparalleled convenience and efficiency in long-distance transportation. One of the most persistent challenges currently facing the aviation industry is the occurrence of flight delays. Such delays have the potential to disrupt travel plans, lead to the incurrence of additional costs, and ultimately result in the dissatisfaction of passengers. In order to address this issue, the objective of this study is to develop a predictive model for flight delays at a Turkish airport using machine learning techniques. Flight delays are commonly defined as instances where flights depart later than their scheduled departure times. It is, however, important to note that the Federal Aviation Administration (FAA) does not categorize delays of 15 min or less as tardy. Accordingly, for the purposes of this study, only those instances of flight delay exceeding the 15 min threshold are considered as delayed, in alignment with the FAA standards [12]. In order to mitigate potential biases and improve the robustness of our models, we explore the use of a resampling technique called Synthetic Minority Over-Sampling Technique (SMOTE). SMOTE is a data augmentation technique that generates synthetic samples by interpolating existing minority class instances, thereby addressing the class imbalance issue. This process helps to prevent models from being biased towards the majority class [13]. In our case, the objective of SMOTE is to achieve a more balanced representation of both on-time and delayed departures by generating synthetic instances of the minority class (delayed flights). There is a paucity of consensus regarding the impact of meteorological conditions on aviation operations. According to the Bureau of Transportation Statistics (2022), while extreme weather conditions affect flight conditions 4% of the times, in 2020, 45.8% of flights were delayed due to weather conditions. The financial impact of flight delays due to weather conditions and other circumstances differs between airlines. In the event of a flight delay resulting from inclement weather, the airline is not required to make additional pay ments to passengers. It is possible that some airlines may attempt to exploit this situation. Consequently, a novel experiment was devised, extracting weather conditions from the available data, with the objective of enhancing the accuracy of flight delay predictions. Feature selection is a crucial aspect of machine learning model development. This study investigates whether comparable predictive results can be achieved using fewer features. In particular, the use of SHAP, a method that automates the selection of relevant features while discarding less informative ones, is explored. This analysis aims to determine if model performance can be optimized by reducing the dimensionality of the dataset without sacrificing predictive accuracy. In conclusion, this article addresses the complex issue of flight delay prediction at a Turkish airport. By considering the guidelines set forth by the FAA, meteorological conditions, data imbalances, and feature selection techniques, our objective is to gain a comprehensive understanding of the factors contributing to flight delays. Ultimately, we aim to develop accurate predictive models that can assist the aviation industry in optimizing operations and enhancing passenger experiences. This study makes several significant contributions to the field of flight delay prediction. Firstly, we conducted an analysis of the performance of various machine learning models, including Logistic Regression, Naive Bayes, Neural Networks, Random Forest, XGBoost, CatBoost, and LightGBM, with the objective of identifying the optimal model for our dataset. This diversity allowed for a comprehensive evaluation of model performance. Secondly, SMOTE was employed in order to address the issue of imbalanced datasets, with the effect of this specific technique being assessed.