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master's thesis
Usporedba metoda strojnog učenja za predviđanje i klasifikaciju u marketingu.
2019. urn:nbn:hr:145:768981
Fabičević, Filip
Josip Juraj Strossmayer University of Osijek
Faculty of Economics and Business in Osijek
Chair of Quantitative Methods and Informatics

Cite this document

Fabičević, F. (2019). Usporedba metoda strojnog učenja za predviđanje i klasifikaciju u marketingu. (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:145:768981

Fabičević, Filip. "Usporedba metoda strojnog učenja za predviđanje i klasifikaciju u marketingu.." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek, 2019. https://urn.nsk.hr/urn:nbn:hr:145:768981

Fabičević, Filip. "Usporedba metoda strojnog učenja za predviđanje i klasifikaciju u marketingu.." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek, 2019. https://urn.nsk.hr/urn:nbn:hr:145:768981

Fabičević, F. (2019). 'Usporedba metoda strojnog učenja za predviđanje i klasifikaciju u marketingu.', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:145:768981

Fabičević F. Usporedba metoda strojnog učenja za predviđanje i klasifikaciju u marketingu. [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek; 2019 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:145:768981

F. Fabičević, "Usporedba metoda strojnog učenja za predviđanje i klasifikaciju u marketingu.", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek, Osijek, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:145:768981

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Metadata
TitleUsporedba metoda strojnog učenja za predviđanje i klasifikaciju u marketingu.
Title (english)Comparison of machine learning methods for prediction and classification in marketing
AuthorFilip Fabičević
MentorMarijana Zekić Sušac (mentor)
Committee memberMarijana Zekić Sušac (predsjednik povjerenstva)
Committee memberJosip Mesarić (član povjerenstva)
Committee memberMartina Briš Alić (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Economics and Business in Osijek
(Chair of Quantitative Methods and Informatics)
Osijek
Defense date and country2019-07-09, Croatia
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Economics
Marketing
Abstract
Prvi cilj rada je izrada modela za predviđanje u marketingu pomoću neuronskih mreža i stabla odlučivanja. Drugi cilj je usporedbom i testiranjem različitih arhitektura metoda strojnog učenja dobiti točniju metodu strojnog učenja. Nadalje, svrha rada je istražiti mogućnosti primjene metoda strojnog učenja u poslovnom odlučivanju u području marketinga Podaci preuzeti iz baze Kaggle klasificirani su i obrađeni prema uspješnosti prodaje putem telefona u programu Statistica. Također, u istraživačkom dijelu opisa i rezultata istraživanja podaci su obrađeni kroz izradu modela neuronskih mreža s testirane dvije aktivacijske funkcije: sigmoidnom i tangens-hiperbolnom te kroz izradu modela stabla odlučivanja. Rezultati dobiveni analizom i usporedbom najtočnijeg modela neuronskih mreža i stabla odlučivanja pokazali su da je model neuronskih mreža testiran pomoću logističke aktivacijske funkcije dao najtočniji rezultat klasifikacije. Iako zbog niske stope točnosti model još nije spreman za primjenu u praksi, moguća su dodatna poboljšanja. Znanstveni doprinos ovoga rada je izrada modela za predviđanje i klasifikaciju u marketingu uporabom metoda strojnog učenja. Dobiveni rezultati rada mogu koristiti za daljnja istraživanja metoda strojnog učenja.
Abstract (english)
The first goal of the paper is to design a prediction model in marketing using neural networks and decision trees. The second goal is to provide a more accurate method of machine learning by comparing and testing different methods of machine learning. Furthermore, the purpose of the paper is to explore the possibilities of applying machine learning methods in business decision making in the field of marketing. The data downloaded from the Kaggle database is classified and processed according to the sales performance over the phone in the Statistica program. Also, in the research part of the description and the results of the research data were processed through the development of models of neural networks with two tested activation functions: sigmoid and tangent-hyperbolic and trough the development of the decision tree model. The results obtained by analyzing and comparing the most accurate model of neural networks and decision trees have shown that the model of neural networks that was tested by logistic activation function gave the most accurate classification result. Although due to the low accuracy rate model is not yet ready for use in practice, further improvements are possible. The scientific contribution of this paper is the development of a model for predicting and classifying in marketing with the use of machine learning methods. The results obtained can be used for further research of machine learning methods.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:145:768981
Study programmeTitle: Business economy; specializations in: Business Informatics
Course: Business Informatics
Study programme type: university
Study level: graduate
Academic / professional title: Magistar/magistra ekonomije (Magistar/magistra ekonomije)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2019-07-17 11:01:04