# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
## 3 Literature Review
As this dataset has not been previously used in the literature, seven different machine learning techniques were applied. The choice of using the ANN technique was driven by its frequent preference in the literature. Naive Bayes (NB) and logistic regression were selected due to their suitability for binary classification tasks. Furthermore, the inclusion of XGBoost, LightGBM, and CatBoost was motivated by their frequent comparison in the context of gradient boosting-based techniques. This study addresses several notable gaps in the existing literature on flight delay prediction. By utilizing a novel dataset from a Turkish airport, which has not been previously explored, the study shows originality and provides a new perspective on flight delay prediction. Furthermore, it conducts a comprehensive comparison of various machine learning models, including Logistic Regression, Naive Bayes, Neural Networks, Random Forest, XGBoost, CatBoost, and LightGBM, allowing for a thorough evaluation of model performance across different scenarios. The integration of weather data into the predictive models enables an examination of the impact of weather conditions on flight delays, offering a more holistic view of the influencing factors. Furthermore, this study addresses data imbalance by employing SMOTE to balance the dataset. This assessment demonstrates an innovative approach to handling data imbalance. Additionally, feature importance \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Data Source** & **Methods** & **Summary** \\ \hline
[44] & U.S. & LR with L2 Regularization, Gaussian NB, KNN, DT, RF & The RF model achieved an accuracy of 82\% when predicting flight delays with a threshold of 15 min \\ \hline
[45] & Automatic Dependent Surveillance Broadcast & LSTM & The proposed model achieved an accuracy of 90.2\% in binary classification \\ \hline
[46] & U.S. & Levenberg–Marquart algorithm & For imbalanced datasets, the accuracy of the 82\% than that of the SAE-LM and SDA models. For balanced datasets, the SDA-LM model’s accuracy was 10.4\% greater than that of the SAE-LM and 7.3\% greater than that of the SDA model \\ \hline
[47] & Airports in New York City & SVM & The variable impact analysis revealed that the following factors are significantly associated with flight departure delay; pushback delay, taxi-out delay, demand–capacity imbalance \\ \hline
[48] & IoT Data Sources & Multiple LR, SVM, Multiple Linear Regression & The study’s results revealed the association between flight delays and air quality index factors. The prediction model achieved an accuracy rate of 85.74\% \\ \hline
[49] & Automatic Dependent Surveillance Broadcast & GB & The experimental results demonstrated that the proposed GBDT-based model produced an accuracy rate of 87.72\% for binary classification \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._analysis using SHAP values identifies key features affecting flight delays, enhancing the interpretability of the models and providing more reliable and understandable predictions for decision-makers.