University of Oulu

Using machine learning to predict smartphone usage

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Author: Kankaanranta, Jyrki1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Department of Computer Science and Engineering, Computer Science
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
Pages: 22
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Language: English
Published: Oulu : J. Kankaanranta, 2023
Publish Date: 2023-01-11
Thesis type: Bachelor's thesis
Tutor: Visuri, Aku


This thesis shows the process of creating and analyzing a machine-learning model. It goes over prevalent classification algorithms and their advantages and disadvantages. Furthermore, techniques and metrics used to evaluate the performance of the model are introduced. In the latter part of the thesis, a Random Forest model is implemented. The objective was to predict the participants’ smartphone usage, more specifically the category of an application they had opened. This starts with a pre-processing phase, where relevant information is extracted from the raw data. Multiple variations of the model are built, and the best-performing model was able to achieve 63.37% accuracy. Additionally, the features are scored to provide more insight into the model. The thesis ends with a brief discussion section, which contemplates the reasons behind the results, some of the model’s deficiencies and how it could be improved.

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Copyright information: © Jyrki Kankaanranta, 2023. Except otherwise noted, the reuse of this document is authorised under a Creative Commons Attribution 4.0 International (CC-BY 4.0) licence ( This means that reuse is allowed provided appropriate credit is given and any changes are indicated. For any use or reproduction of elements that are not owned by the author(s), permission may need to be directly from the respective right holders.