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Application of machine learning algorithms for classification and regression problems for mobile game monetization

  • Many companies use machine learning techniques to support decision-making and automate business processes by learning from the data that they have. In this thesis we investigate the theory behind the most widely used in practice machine learning algorithms for solving classification and regression problems. In particular, the following algorithms were chosen for the classification problem: Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM), Learning Vector Quantization (LVQ). As for the regression problem, Decision Trees, Random Forest and Gradient Boosted Tree were used. We then apply those algorithms to real company data and compare their performances and results.

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Metadaten
Author:Olga Isakova
Advisor:Thomas Villmann, Marika Kaden
Document Type:Master's Thesis
Language:English
Year of Completion:2019
Granting Institution:Hochschule Mittweida
Release Date:2020/07/13
GND Keyword:Maschinelles Lernen
Institutes:Angewandte Computer‐ und Bio­wissen­schaften
Dewey Decimal Classification:006.31 Maschinelles Lernen
Open Access:Frei zugänglich
Licence (German):License LogoEs gilt das UrhG