Machine Learning Techniques for analysis of Egyptian Flight Delay
Mohamed, H., Al-Tabbakh, S., El-Zahed, H. (2018). Machine Learning Techniques for analysis of Egyptian Flight Delay. EKB Journal Management System, 35(part 1), 390-399. doi: 10.21608/jsrs.2018.25523
hanaa Maher Mohamed; Shahinaz M. Al-Tabbakh; H. El-Zahed. "Machine Learning Techniques for analysis of Egyptian Flight Delay". EKB Journal Management System, 35, part 1, 2018, 390-399. doi: 10.21608/jsrs.2018.25523
Mohamed, H., Al-Tabbakh, S., El-Zahed, H. (2018). 'Machine Learning Techniques for analysis of Egyptian Flight Delay', EKB Journal Management System, 35(part 1), pp. 390-399. doi: 10.21608/jsrs.2018.25523
Mohamed, H., Al-Tabbakh, S., El-Zahed, H. Machine Learning Techniques for analysis of Egyptian Flight Delay. EKB Journal Management System, 2018; 35(part 1): 390-399. doi: 10.21608/jsrs.2018.25523

Machine Learning Techniques for analysis of Egyptian Flight Delay

Journal of Scientific Research in Science
Article 5, Volume 35, part 1, August 2018, Page 390-399  XML PDF (722.38 K)
Document Type: Original Article
DOI: 10.21608/jsrs.2018.25523
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Authors
hanaa Maher Mohamed email 1; Shahinaz M. Al-Tabbakh2; H. El-Zahed3
11Internet Dev.Dept. Manager of IT Sector,EGYPTAIR Holding Cooperation, Cairo, Egypt
2Computer Science Group, Faculty of Women for Sciences, A. and Education, Ain Shames University, Cairo-Egypt.
3Faculty of Women for Sciences, A. and Education, Ain Shames University, Cairo-Egypt.
Abstract
Flight delay has been the fiendish problem to the world's aviation industry, so there is very important significance to research for computer system predicting flight delay propagation. Extraction of hidden information from large datasets of raw data could be one of the ways for building predictive model. This paper describes the application of classification techniques for analysing the Flight delay pattern in Egypt Airline’s Flight dataset.In this work, four decision tree classifiers were evaluated and results show thatthe REPTree have the best accuracy 80.3%with respect to Forest, StumpandJ48.However, four rules based classifiers were compared and results show that PART provides best accuracy amongstudied rule-based classifiers withaccuracy of 83.1%.By analysing runningtime for all classifiers, the current work concluded that REPtree is the most efficient classifier with respect to accuracy and running time. Also,thecurrent work is extended to apply of Apriori association technique to extract some important information about flight delay. Association rules are presented and association technique is evaluated.
 
 
Keywords
Airlines; Flight delay; WEKA; Bigdata; Data mining; classification Algorithms; J48; Random Forest; Decision Stump; Ripper rule; Association Rules; Apriori; Confusion matrix
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