# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
## 3 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Data Source** & **Methods** & **Summary** \\ \hline
[33] & Github & RF, DT, Support Vector Machine (SVM), Naive Bayes (NB) & The analysis revealed that DT achieved the highest accuracy with a score of 0.70 \\ \hline
[34] & Egypt Air flight delay data & REPTree, Forest, Stump, J48 & REPTree achieved the highest accuracy of 80.3\%. In the case of rule-based classifiers, PART demonstrated the best accuracy, reaching 83.1\%. Additionally, when considering both accuracy and running time, REPTree emerged as the most efficient classifier \\ \hline
[35] & 70 Airports in the U.S. & Logistic Regression (LR) & The findings of the study revealed that the proposed approach achieved an accuracy rate of approximately 80\% \\ \hline
[36] & Brazilian National Civil & ANN, RF, SVM, NB, KNN & The findings demonstrated that the incorporation of balancing techniques into the models led to a noteworthy enhancement resulting in an accuracy rate of approximately 60\% \\ \hline
[37] & Beijing International Airport & Deep Belief Network & The model incorporated support vector regression (SVR) for refinement. The results revealed that when utilizing the DBN-SVR model, approximately 99.3\% of the predicted values fell within a 25 cmin deviation from the observed values \\ \hline
[38] & U.S. & RF, Delay Propagation Model & The study employed an air traffic delay prediction model that integrated RF with an approximate delay propagation model. The model achieved a departure delay accuracy of 0.83 and an arrival delay accuracy of 0.87 \\ \hline
[39] & Air traffic data systems &
\begin{tabular}{c} \\ DT, NB, Classification Rule Learners, SVM, Bagging Ensemble, Boosting Ensemble, RF \\ \end{tabular} & The study employed supervised Machine Learning algorithms to construct prediction models for both weather-related and volume-related Ground Delay Programs. The research identified the Boosting Ensemble algorithm as the most effective, achieving the highest Kappa statistic of 0.66 \\ \hline
[40] & Low-cost airlines in Europe & LSTM, LR, BM, TL & The study highlighted the potential for small airlines to initiate model training through transfer learning and subsequently fine-tune the models using their own data \\ \hline
[41] & U.S. and Iranian data & RF, DT & The proposed approach reached 71.39\% for predicting delay occurrences and 70.16\% for predicting delay magnitude in the U.S. network, while it achieved 76.44\% and 75.93\%, respectively, in the Iranian network \\ \hline
[42] & U.S. & LR, DT Regression, Bayesian Ridge, RF, Gradient Boosting Regression & It was shown that the RF Regressor was the best-performing model for predicting both departure and arrival delays, as it demonstrated the lowest mean squared error and mean absolute error \\ \hline
[43] & U.S. & CatBoost & The model achieved an accuracy of 80.44\%, indicating its effectiveness in predicting flight arrival delays \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._In conclusion, each method for flight delay prediction has its own set of strengths and weaknesses. Regression models and probability-based approaches offer simplicity and flexibility but may encounter difficulties when faced with complex relationships and data requirements. Operations research models offer powerful optimization capabilities but can be complex and difficult to scale. Network-based models offer a comprehensive view of delay propagation but are data-intensive and computationally demanding. Machine learning models are distinguished by their high accuracy and capacity to handle complex relationships. However, these models require substantial data and computational resources. In the digital age, data are widely acknowledged as a form of currency. Consequently, it is of paramount importance for businesses to make predictions based on their own data sources. In this context, flight data belonging to a single airport operator were utilized in this study.