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undergraduate thesis
Uporaba metoda nenadziranog strojnog učenja za potrebe analize navika kupaca
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:345760
Grujić Ostojić, Petra Dunja
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Grujić Ostojić, P. D. (2022). Uporaba metoda nenadziranog strojnog učenja za potrebe analize navika kupaca (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:345760

Grujić Ostojić, Petra Dunja. "Uporaba metoda nenadziranog strojnog učenja za potrebe analize navika kupaca." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:345760

Grujić Ostojić, Petra Dunja. "Uporaba metoda nenadziranog strojnog učenja za potrebe analize navika kupaca." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:345760

Grujić Ostojić, P. D. (2022). 'Uporaba metoda nenadziranog strojnog učenja za potrebe analize navika kupaca', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:345760

Grujić Ostojić PD. Uporaba metoda nenadziranog strojnog učenja za potrebe analize navika kupaca [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2025 January 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:345760

P. D. Grujić Ostojić, "Uporaba metoda nenadziranog strojnog učenja za potrebe analize navika kupaca", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:345760

Metadata
TitleUporaba metoda nenadziranog strojnog učenja za potrebe analize navika kupaca
Title (english)Using Unsupervised Machine Learning Methods for Analyzing Customer Habits
AuthorPetra Dunja Grujić Ostojić
MentorDamir Pintar (mentor)
Committee memberDamir Pintar (predsjednik povjerenstva)
Committee memberMatija Šulc (član povjerenstva)
Committee memberLuka Humski (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Osnovni zadatci nenadziranog strojnog učenja su grupiranje, procjena gustoće, otkrivanje anomalija i stršećih vrijednosti i smanjenje dimenzionalnosti. Nenadzirano strojno učenje ima veliku primjenu u marketingu. Danas to područje obiluje podatcima pomoću kojih tvrtke mogu razumjeti potrebe kupaca i steći njihovu naklonost, a time i dugoročni profit. U ovom je radu naglasak na metodama grupiranja kupaca na temelju njihovih karakteristika i potrošačkih navika. Na odabranom podatkovnom skupu provedena je eksploratorna analiza, reducirana dimenzionalnost te su primijenjeni algoritmi K-sredina, K-medoida i algoritam hijerarhijskog grupiranja (HAC). Uočeno je da na ovom podatkovnom skupu algoritam K-sredina dao bolje rezultate od algoritma K-medoida: dobivene 3 grupe su bile homogenije. Na temelju razlika između grupa kupaca tvrtka bi mogla personalizirati buduću marketinšku kampanju.
Abstract (english)
Unsupervised machine learning models are utilized for four main tasks: clustering, density estimation, anomalies and outliers detection, and dimensionality reduction. Nowadays, there's a lot of data that can be used to satisfy customer needs and sustain a profitable business. Therefore, unsupervised machine learning plays a major role in marketing. The main focus of this paper is customer clustering techniques. Before performing customer segmentation based on their characteristics and purchase behavior, an exploratory analysis of chosen dataset has been done. Clustering algorithms that were used in this paper are the K-means algorithm, the K-medoids algorithm, and hierarchical agglomerative clustering (HAC). It turned out that the K-means algorithm outperformed the K-medoids algorithm on this dataset: 3 obtained clusters were more homogeneous. Based on differences among clusters company could personalize its future marketing campaign.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:345760
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-15 10:15:00