Predicting Steam Games Rating with Regression

Abstract

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.