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Predicting Operator’s Choice During Airline Disruption Using Machine Learning Methods
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
2019 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

This master thesis is a collaboration with Jeppesen, a Boeing company to attempt applying machine learning techniques to predict “When does Operator manually solve the disruption? If he chooses to use Optimiser, then which option would he choose? And why?”. Through the course of this project, various techniques are employed to study, analyze and understand the historical labeled data of airline consisting of alerts during disruptions and tries to classify each data point into one of the categories: manual or optimizer option. This is done using various supervised machine learning classification methods.

sted, utgiver, år, opplag, sider
2019.
Emneord [en]
Machine Learning, supervised learning, Classification
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-18839OAI: oai:DiVA.org:bth-18839DiVA, id: diva2:1367109
Eksternt samarbeid
Jeppesen, A boeing company
Fag / kurs
DV2572 Master´s Thesis in Computer Science
Utdanningsprogram
DVADA Master Qualification Plan in Computer Science
Veileder
Examiner
Tilgjengelig fra: 2019-11-04 Laget: 2019-10-31 Sist oppdatert: 2019-11-04bibliografisk kontrollert

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Predicting Operator’s(2383 kB)69 nedlastinger
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