| Issue |
E3S Web of Conf.
Volume 388, 2023
The 4th International Conference of Biospheric Harmony Advanced Research (ICOBAR 2022)
|
|
|---|---|---|
| Article Number | 02001 | |
| Number of page(s) | 6 | |
| Section | Big Data, Green Computing, and Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202338802001 | |
| Published online | 17 May 2023 | |
Predicting Steam Games Rating with Regression
Computer Science Department, Faculty of Computing and Media, 11480 Bina Nusantara University, Indonesia
* Corresponding author: andreas.teja@binus.ac.id
This paper tries to find out the best regression model to predict the rating of video games. It is done by comparing multiple variables related to Metascore, such as genres and player count. In order to be able to get accurate results, we gather some data by scraping them from Steam and combining them with public data. The games in this study are from Steam since it is one of the largest computer video games distributors. In this study, we evaluate several regression models, such as Linear regression, Decision Tree, Random Forest to predict the game rating. The experiment shows that tree-based regression model, such as LightGBM and Random Forest performed better than any other regression method, with R2 score above 0.9.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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