Change search
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
Maskininlärning inom kundanalys: Prediktion av kundbeteende inom energibranchen
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
2019 (Swedish)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesisAlternative title
Machine learning for customer analysis : Predicting customer churn in the electricity distribution sector (English)
Abstract [en]

This thesis considers the problem of churn within the electricity distribution sector. More specifically, this study evaluates how supervised machine learning can be used by a Swedish electricity distributor in order to identify customer churn. The data was by provided by the electricity distributor and covered personal, geographical and contract specific information regarding the company’s customers. The provided data was complemented with external data covering the customers’ financial positions. Based on this information the possibility to predict customer churn over a three-month period with a gradient boosted decision tree was evaluated. The results from the proposed models suggests that the possibility to identify customer churn is rather poor and could not be used in a practice. This is believed to be a result of unbalanced class distributions and that the data provided simply is not informative enough to accurately predict customer churn. If more information about the customers is collected, with predictive analyses in mind, the performance of the model is likely to increase.

Place, publisher, year, edition, pages
2019. , p. 81
Series
UPTEC STS, ISSN 1650-8319 ; 19004
Keywords [sv]
maskininlärning, klassificering, churn
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-376295OAI: oai:DiVA.org:uu-376295DiVA, id: diva2:1285364
External cooperation
Svenska Business Vision AB
Educational program
Systems in Technology and Society Programme
Supervisors
Examiners
Available from: 2019-02-06 Created: 2019-02-04 Last updated: 2019-02-06Bibliographically approved

Open Access in DiVA

fulltext(1368 kB)44 downloads
File information
File name FULLTEXT01.pdfFile size 1368 kBChecksum SHA-512
bae02a624753ad1f5375f7aea75459cdf3328fd7e43f20ef616965e826885cb2527a4f7d70f3a77a223f0fb9354b7d56bf6135ac88b2a70b29e2571e096edb74
Type fulltextMimetype application/pdf

By organisation
Division of Systems and Control
Other Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 44 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 113 hits
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