# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
## 7 Conclusions  
In this section, we interpret and analyze the results of our study on flight delay prediction at a Turkish airport using machine learning techniques.  
Looking at the results, based on the accuracy rates reported in the literature, XGBoost, CatBoost, and LightGBM performed consistently well across different scenarios. They consistently maintained robust performance when dealing with unbalanced data, integrating weather information, and when SMOTE was applied. Specifically, XGBoost achieved an accuracy of 80% and recall rates of 20% to 27% across different trials, indicating its robustness. CatBoost and LightGBM also demonstrated strong performance, with accuracies around 80% and recall rates ranging from 0.13 to 0.24. Logistic Regression, although generally less robust compared to more advanced models, showed improved performance when SMOTE was applied. With only airport data and SMOTE, Logistic Regression achieved an accuracy of 64% and 61% on the test set. When both weather and airport data were used with SMOTE, the accuracy improved to 65% on the test set, with recall rates of 60%. In the literature, most studies report results based primarily on accuracy. In this context, our findings suggest that XGBoost, CatBoost, and LightGBM, with their higher accuracy rates, are more effective for predicting flight delays compared to Logistic Regression. Therefore, they appear to be strong candidates for predicting flight delays at this Turkish airport.  
In the context of predicting delayed flights on time, the most unfavorable scenario would be a model that performs poorly in terms of recall. In this context, the least desirable outcome would be that the model failed to correctly identify a significant proportion of delayed flights (i.e., high number of false negatives). XGBoost consistently demonstrated higher recall rates across a range of trials and scenarios. Nevertheless, the performance of the models on the test dataset, while satisfactory, indicates a need for further improvement in terms of generalization. This finding indicates that our models may benefit from additional fine-tuning and evaluation to enhance their performance in real-world settings.  
The incorporation of weather data into our models proved to be a valuable addition, as it provided insights into the impact of weather on flight delay predictions. The experiments demonstrated that the weather conditions exert some influence on flight delays. Nevertheless, while the incorporation of weather data enhanced the model performance to a certain extent, it did not markedly alter the overall prediction accuracy in comparison to that of models that did not include weather data. This suggests that while the weather conditions are a significant factor, airport-specific operational factors may be more influential in determining in-flight delays at the studied location. Future research could investigate the interplay between weather conditions and operational factors in greater depth in order to gain a more comprehensive understanding of their combined effect on flight delays. Furthermore, the limited impact of the weather data on overall prediction accuracy indicates the potential necessity for more granular weather data or the investigation of other environmental factors that might affect flight operations.  
Feature selection plays a critical role in model performance and interpretability. Our analysis of feature importance using SHAP values indicated that several key features significantly influence flight delays. Factors such as scheduled time, number of passengers, time to arrival, atmospheric pressure at the arrival airport, travel distance, and the number of flights departed from the airport showed the highest impacts on predicting flight delays. Understanding these influences is essential for aviation professionals to optimize operations and passenger experiences.