A Comparative Analysis for Predicting Airline Arrival Delays

Flight data is a large source of big data. Million
flights are delayed or canceled each year due to several factors.
Study aviation systems are being significant to the economy which
improves customer satisfaction, and saves time. Delay Prediction
in aviation systems is somewhat complicated because of the large
volume of data, the multiple causes of delays. The reasons vary
from region to region and from company to another. In this paper,
we compare the performance of different machine learning
approaches (Random Forest Classifier, logistic regression,
Gaussian Naive Bayes and Decision Tree Classifier) for predicting
the arrival delay depending on the multiple characteristics and
mention the features in each approach. Using machine-learning
toolkit supported on the Splunk platform to make a comparison
between them. The Airline On-Time Performance Data are used
for evaluating the models. The results demonstrate that the
Logistic regression is better than others and works well with
discrete data.