Machine Learning Techniques for Analysis of Egyptian Flight Delay

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 that the REPTree have the best accuracy 80.3% with respect to Forest, Stump and J48. However, four rules based classifiers were compared and results show that PART provides best accuracy among studied rule-based classifiers with accuracy of 83.1%. By analysing running time for all classifiers, the current work concluded that REPtree is the most efficient classifier with respect to accuracy and running time. Also, the current 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.