Predicting Game Addiction Using Machine Learning

Abstract

The main purpose of the paper is to determine how online game affects different aspects of Bangladeshi university-going students’ life. To find out the ratio or addiction rate of gaming on these young generation or students used data science. Mainly, using the machine learning algorithms tried to find out the addiction rate or ratio. The main research questions are how much time they spent playing online or offline based games, how much money they spent on them and what influences them to play online or offline games. In other countries, they have already started working on this side and tried to take necessary steps for the addicted young generation. But, in our country there was previous work on this side and the rate was too high during and after the Covid-19 pandemic. Though the rate was high, no one considered this issue as a serious issue, nor took any steps to solve this issue. Thus, the rate increased day by day and created a huge impact on the young generation. To solve the issue and to show to actual data or report this work was started and created an impactful result which will help others to take the proper steps for decreasing game addiction. To complete this work here total five steps were followed which are: Filtering, Clearing, Processing, Sorting and Testing. This is also a psychological case so took help from the site to find out the gaming addiction level or parameter. Data collection was done through online and social platform-based questionnaire form. A total of 365 participants provided the data. The primary findings of the study were that most of the participants play 2-4 hours daily, but they don’t want to spend money buying paid games. Another interesting finding was that although participants were casual gamers, they were willing to play the game. The significance of this work is also important. Though this is the work in this country and the success rate was great so anyone can use the machine learning algorithms to check out the addiction level and for future study or research-based works. Also, the government and other non-govt. organizations can use algorithms to detect the addiction level in the young generation.