Repository logo
 

Unsupervised Machine Learning Models for User Segmentation in a Mobile App : Evaluating the Effectiveness

Simple item page
dc.contributor.advisorArtinger, Florian
dc.contributor.advisorKoç, Hasan
dc.contributor.authorElena Sidorenko
dc.contributor.departmentFaculty of Business Administration
dc.contributor.otherBerlin International University of Applied Sciences
dc.date.accessioned2024-01-15
dc.date.accessioned2025-11-28T13:27:24Z
dc.date.available2025-11-28T13:27:24Z
dc.date.issued2023
dc.description.abstractAI-GENERATED ABSTRACT: Abstract: Context: User segmentation is an effective tool to understand your users' needs and make data-driven decisions. With limited resources many startups have. Would user clustering with unsupervised machine learning models be effective? Objectives: The thesis consists of a literature review, two interviews, and data analysis. It evaluates how effective the different unsupervised models are in application to user segmentation in a mobile application. Methods: a convenience sample interview was conducted with the chief executives of a startup to get an understanding of primary goals and objectives. Data were analyzed with the application of dimension reduction (PCA, t-SNE, UMAP, correlation coefficient, variance threshold), clustering (K-Means, DBSCAN), and supervised models for predictive analysis (Random Forest, Lasso, Logistic Regression). Results: It was possible to identify 4 different clusters of users within the app with unique behavior. Conclusion: The application of PCA, K-Means, and Random Forest was the most effective for a highly dimensional dataset. This user segmentation was valuable, but not new to the chief executives to the company. Meaningful insights were drawn from data analysis. Keywords: user segmentation, unsupervised learning, machine learning, mobile application, PCA, K-Means clustering, Random Forest, data analysis, startups, user behavior clusteringen
dc.description.degreeBA
dc.description.tableofcontentsMACHINE-GENERATED CONTENTS NOTE: Table of Contents abstract..........................................................................................................2 introduction....................................................................................................3 literature Review..........................................................................................6 2.1 Non-machine Learning User Segmentation Techniques..................7 2.2 Supervised Versus Unsupervised Machine Learning.........................7 2.2.1 Supervised Learning................................................................8 2.2.2 Unsupervised Learning..............................................................8 2.3 Customer Segmentation With Machine Learning...........................12 2.4 User Segmentation in Social Media Platforms..............................13 2.4.1 Case of Pinterest.................................................................13 2.4.2 Case of Instagram.................................................................13 2.5 Methodological Considerations..............................................14 methodology...............................................................................................15 the Research Hypotheses.........................................................................15 Research Design.....................................................................................15 Literature Review...................................................................................16 Interview No1.........................................................................................16 Data Analysis........................................................................................17 Data Collection......................................................................................17 Data Cleaning and Dimension Reduction..........................................17 Clustering Algorithms...........................................................................20 Predictive Models................................................................................22 Interview 2............................................................................................22 results...........................................................................................................23 Evaluation of Interview No1...................................................................23 Data Analytics.......................................................................................24 Cluster Analysis...................................................................................25 Predictive Models................................................................................28 Evaluation of Interview No2.................................................................29 discussion....................................................................................................30 conclusion...................................................................................................33 references...................................................................................................35 appendix......................................................................................................40 Section A.............................................................................................40 Section B.............................................................................................49 appendix 2..................................................................................................50en
dc.identifier.urihttps://repository.berlin-international.de/handle/123456789/802
dc.language.isoeng
dc.subjectData Analysis
dc.subjectK-Means Clustering
dc.subjectMachine Learning
dc.subjectMobile Application
dc.subjectPca
dc.subjectRandom Forest
dc.subjectUnsupervised Learning
dc.subjectUser Behavior Clustering
dc.subjectUser Segmentation
dc.titleUnsupervised Machine Learning Models for User Segmentation in a Mobile App : Evaluating the Effectiveness
dc.typeThesis

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
60912.pdf
Size:
142 B
Format:
Adobe Portable Document Format
Download

Collections

Undergraduate Theses
*formerly known as BAU International Berlin - University of Applied Sciences
©2024 Berlin International University of Applied Sciences |Legal Notice|Privacy policy