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Predicting Operator’s Choice During Airline Disruption Using Machine Learning Methods
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
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.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Machine Learning, supervised learning, Classification
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18839OAI: oai:DiVA.org:bth-18839DiVA, id: diva2:1367109
External cooperation
Jeppesen, A boeing company
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVADA Master Qualification Plan in Computer Science
Supervisors
Examiners
Available from: 2019-11-04 Created: 2019-10-31 Last updated: 2019-11-04Bibliographically approved

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Predicting Operator’s(2383 kB)17 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf