Uporaba metoda nenadziranog strojnog učenja za potrebe analize navika kupaca

undergraduate thesis

• Title (english): Using Unsupervised Machine Learning Methods for Analyzing Customer Habits
• Author: Petra Dunja Grujić Ostojić
• Mentor: Damir Pintar (mentor)
• Committee member: Damir Pintar (predsjednik povjerenstva)
• Committee member: Matija Šulc (član povjerenstva)
• Committee member: Luka Humski (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2022-07-12, 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: nenadzirano strojno učenje; grupiranje; analiza glavnih komponenata; segmentacija kupaca; analiza navika kupaca; analiza osobnosti kupaca; marketing; eksploratorna analiza podataka; algoritam K-sredina; algoritam K-medoida; hijerarhijsko aglomerativno grupiranje
• Keywords (english): unsupervised machine learning; clustering; Principal Component Analysis (PCA); customer segmentation; customer behavior analysis; customer personality analysis; marketing; exploratory data analysis; the K-Means algorithm; the K-Medoids algorithm; hierachical agglomerative clusering (HAC)
• Language: croatian
• URN:NBN: urn:nbn:hr:168:345760
• Study programme: Title: 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 resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-15 10:15:00