| A Comparative Analysis for Predicting Airline Arrival Delays |
| Paper ID : 1041-ICCI2021 (R2) |
| Authors: |
|
ِAlaa Ibrahem *1, hamdi mousa2, heba elbeh3 1computer science ,Faculty of Computing and Information, menoufia university, 2Computer Science Department Faculty of Computers and Information Menoufia University 3computer science ,faculty of science |
| Abstract: |
| Flight data is a large source of big data and a million flights are delayed or canceled each year due to several factors. Study aviation systems is significant to the economy, improves customer satisfaction and saves time. Delay Prediction in aviation systems is somewhat complicated because of large volume of data, the multiple causes of delays and 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 Naïve 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 in 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. |
| Keywords: |
| Predicting - Airline Arrival Delays - Models - Splunk - Data analysis. |
| Status : Paper Accepted |
