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Binary classification for predicting propensity to buy flight tickets: A study on whether binary classification can be used to predict Scandinavian Airlines customers' propensity to buy a flight ticket within the next seven days.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Binär klassificering applicerat på att prediktera benägenhet att köpa flygbiljetter (Swedish)
Abstract [en]

A customers propensity to buy a certain product is a widely researched field and is applied in multiple industries. In this thesis it is showed that using binary classification on data from Scandinavian Airlines can predict their customers propensity to book a flight within the next coming seven days. A comparison between logistic regression and support vector machine is presented and logistic regression with reduced number of variables is chosen as the final model, due to it's simplicity and accuracy. The explanatory variables contains exclusively booking history, whilst customer demographics and search history is showed to be insignificant.

Place, publisher, year, edition, pages
2019. , p. 37
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:umu:diva-162412OAI: oai:DiVA.org:umu-162412DiVA, id: diva2:1344012
Educational program
Master of Science in Engineering and Management
Presentation
2019-06-05, NA430, Naturvetarhuset, Umeå, 09:39 (English)
Supervisors
Examiners
Available from: 2019-10-15 Created: 2019-08-20 Last updated: 2019-10-15Bibliographically approved

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fulltext(574 kB)1841 downloads
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Mazouch, MarcusAndersson, Martin
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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