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Product categorisation using machine learning
KTH, School of Technology and Health (STH), Medical Engineering, Computer and Electronic Engineering.
KTH, School of Technology and Health (STH), Medical Engineering, Computer and Electronic Engineering.
2017 (English)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesisAlternative title
Produktkategorisering med hjälp av maskininlärning (Swedish)
Abstract [en]

Machine learning is a method in data science for analysing large data sets and extracting hidden patterns and common characteristics in the data. Corporations often have access to databases containing great amounts of data that could contain valuable information.

Navetti AB wants to investigate the possibility to automate their product categorisation by evaluating different types of machine learning algorithms. This could increase both time- and cost efficiency.

This work resulted in three different prototypes, each using different machine learning algorithms with the ability to categorise products automatically. The prototypes were tested and evaluated based on their ability to categorise products and their performance in terms of speed. Different techniques used for preprocessing data is also evaluated and tested.

An analysis of the tests shows that when providing a suitable algorithm with enough data it is possible to automate the manual categorisation. 

Abstract [sv]

Maskininlärning är en metod inom datavetenskap vars uppgift är att analysera stora mängder data och hitta dolda mönster och gemensamma karaktärsdrag. Företag har idag ofta tillgång till stora mängder data som i sin tur kan innehålla värdefull information.

Navetti AB vill undersöka möjligheten att automatisera sin produktkategorisering genom att utvärdera olika typer av maskininlärnings- algoritmer. Detta skulle dramatiskt öka effektiviteten både tidsmässigt och ekonomiskt.

Resultatet blev tre prototyper som implementerar tre olika maskininlärnings-algoritmer som automatiserat kategoriserar produkter. Prototyperna testades och utvärderades utifrån dess förmåga att kategorisera och dess prestanda i form av hastighet. Olika tekniker som används för att förbereda data analyseras och utvärderas.

En analys av testerna visar att med tillräckligt mycket data och en passande algoritm så är det möjligt att automatisera den manuella kategoriseringen. 

Place, publisher, year, edition, pages
2017. , 38 p.
Series
TRITA-STH, 2017:39
Keyword [en]
Machine learning, SVM, Naive Bayes, DBSCAN
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-209031OAI: oai:DiVA.org:kth-209031DiVA: diva2:1109910
External cooperation
Navetti AB
Subject / course
Computer Science
Educational program
Bachelor of Science in Engineering - Computer Engineering
Supervisors
Examiners
Available from: 2017-06-16 Created: 2017-06-14 Last updated: 2017-06-16Bibliographically approved

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CiteExportLink to record
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