Airline flight delay prediction using Naïve Bayes algorithm / Ahmad Adib Baihaqi Shukri

The aviation industry plays a critical role in global transportation, facilitating economic growth and revolutionizing travel. However, flight delays have become a growing concern, impacting both airlines and passengers. This study aims to study the Naïve Bayes algorithm for flight delay prediction. The objective is to develop a reliable flight delay prediction model using the Naïve Bayes algorithm and evaluate its performance. The data set that records flight delay and cancellation data from U.S Department of Transportation’s (DOT) was used for the prediction. Three algorithms (Gaussian Naïve Bayes, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM)) were trained and tested to complete the binary classification of flight delays. Parameter tuning also done on Gaussian Naïve Bayes by changing its parameter. The evaluation of algorithms was fulfilled by comparing the values of accuracy, specificity and ROC AUC score. These measures were weighted to adjust the imbalance of the selected data set. The comparative analysis showed that the Gaussian Naïve Bayes has the best performance with an accuracy of 93% and KNN has the worst performance with ROC AUC score 63%. The Naïve Bayes classifier generally have better performance over other base classifiers.