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Predicting sales in a foodstore department using machine learning
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Försäljningsprediktion i en matvarubutik med hjälp av maskininlärning (Swedish)
Abstract [en]

Prediction of sales is an important field in the food industry and dueto new technologies it has recently gained alot of attention in order toimprove business operations and profitability. However, historicallythe industry has relied on traditional statistical models but in recentyears more advanced machine learning methods has gained traction.This study aims to compare three machine learning methods forsales prediction in the food industry: Multilayer Perceptron (MLP),Support Vector Machine (SVM) and Radial Basis Function Network(RBFN). The methods were compared in terms of their prediction accu-racy on daily sales in a food store department. The performance of themodels was determined using the performance measures: Mean Aver-age Percentage Error (MAPE) and Root Mean Squared Error (RMSE).The results show that the SVM performed lower error measuresthan the other two methods. The repeated measure analysis of vari-ance (rANOVA) was used in order to determine if there was a differ-ence between the methods. The test indicated a statistical significantdifference between the afformentioned methods.

Abstract [sv]

Försäljningsprediktion är ett viktigt område inom livsmedelsindustrin och tack vare nya teknologier har området nyligen fått stor uppmärksamhet i syfte att förbättra affärsverksamheten och lönsamheten i en mataffär. Historiskt sett har livsmedelsindustrin dock använt sig av traditionella statistiska modeller men under senare år har mer avancerade maskininlärningsmetoder vunnit mark. Denna studie ämnar att jämföra tre maskininlärningsmetoder för försäljningsprognostisering inom livsmedelsindustrin: Multilayer Perceptron med hjälp av backpropagation (MLP), Support Vector Machine (SVM) och Radial Basis Function Network. Metoderna jämfördes med avseende på deras prediktionsträffsäkerhet på daglig försäljning i en butiksavdelning och mättes med hjälp av mätningsvärktygen: medelprocentfelet (MAPE) och rotmedelfelet (RMSE). Resultaten visar att SVM presterade lägre prediktionsfel än de andra två metoderna. Upprepad variansanalys (rANOVA) användes för att avgöra om det förelåg någon skillnad mellan metoderna. Testet indikerade en statistiskt signifikant skillnad mellan metoderna.

Place, publisher, year, edition, pages
2017.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-208888OAI: oai:DiVA.org:kth-208888DiVA: diva2:1108597
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Available from: 2017-06-17 Created: 2017-06-12 Last updated: 2017-06-17Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
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  • sv-SE
  • Other locale
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Output format
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