International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Machine Learning Models in Predicting Gaming Popularity: A Comparative Analysis

Author(s) KV Vidath Vardhan
Country India
Abstract This paper provides an insightful exploration into the application of machine learning (ML) in predicting the player base of video games, shedding light on the pivotal role ML plays in the gaming industry. The introductory section succinctly defines machine learning and its relevance, emphasising its ability to analyse patterns and make data-driven predictions. Focusing specifically on the gaming sector, the paper delves into the significant impact of ML on understanding player behaviour, optimising user experiences, and enhancing overall game performance. Three prominent ML models — Logistic Regression, Decision Tree and Random Forest — are comprehensively examined for their efficacy in forecasting the base number of players owning a game. To validate the models, a publicly available dataset is employed and the study aims to unravel the strengths and weaknesses of each model, offering valuable insights for developers and stakeholders in the gaming industry. Random forest model emerged as the one with maximum accuracy of 89.38%. The paper contributes to the growing body of knowledge on ML applications in gaming, showcasing the potential of predictive analytics in anticipating and meeting player demands. 
Keywords artificial intelligence, machine learning, robots, types of learning.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-06-10
Cite This Machine Learning Models in Predicting Gaming Popularity: A Comparative Analysis - KV Vidath Vardhan - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.14362
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.14362
Short DOI https://doi.org/gtzjq8
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