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dc.contributor.authorDand, Alok
dc.contributor.authorSaeed, Khawaja A.
dc.contributor.authorYildirim, Mehmet Bayram
dc.date.accessioned2019-11-06T16:29:38Z
dc.date.available2019-11-06T16:29:38Z
dc.date.issued2019
dc.identifier.citationDand, Alok; Saeed, Khawaja A.; Yildirim, Mehmet Bayram. 2019. Prediction of airline delays based on machine learning algorithms. 25th Americas Conference on Information Systems, AMCIS 2019; Cancun International Convention CenterCancun; Mexico; 15 August 2019 through 17 August 2019; Code 151731en_US
dc.identifier.urihttps://aisel.aisnet.org/cgi/viewcontent.cgi?article=1379&context=amcis2019
dc.identifier.urihttp://hdl.handle.net/10057/16763
dc.descriptionClick on the URI to access the article (may not be free).en_US
dc.description.abstractEvery year around 20% of all flights are delayed or canceled. The costs of these events to the airline companies and passengers are in billions of dollars each year. According to a report published in 2010 by UC Berkeley, Federal Aviation Agency (FAA), and National Center of Excellence for Aviation Operations Research (NEXTOR), around half of the total cost (direct and indirect) is paid by the passenger. The goal of this study is to predict airline delays using binary supervised and unsupervised machine learning classification algorithms using the US Domestic Flights data from 2015-2017. We use an expanded set of variables some of which are subject to decisional control of the airlines to help improve the predictions. Different performance measures such as prediction accuracy, recall rates, receiver operation characteristics – area under the curve scores were used to evaluate the efficacy of different algorithms.en_US
dc.language.isoen_USen_US
dc.publisherAssociation for Information Systemsen_US
dc.relation.ispartofseries25th Americas Conference on Information Systems, AMCIS 2019;
dc.subjectAirline delay predictionen_US
dc.subjectData analyticsen_US
dc.subjectMachine learningen_US
dc.subjectPredictionen_US
dc.titlePrediction of airline delays based on machine learning algorithmsen_US
dc.typeConference paperen_US
dc.rights.holder© 2019 Association for Information Systems. All rights reserved.en_US


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