A machine learning-based approach for predicting the impact of video games on youth

Abstract

The abstract of the topic "impact of video games prediction on youth using machine learning" would succinctly summarize the key components of the research. This research explores the intersection of video games, youth development, and machine learning, investigating how predictive models can unveil intricate patterns in gaming behaviors. By analyzing cognitive, academic, and mental health impacts, as well as social dynamics, the study aims to inform responsible gaming practices and educational strategies. Ethical considerations and long-term societal implications are integral to the examination, emphasizing the need for a balanced approach in navigating the digital landscape for the well-being of the youth. The study has the motive to predict whether a person who plays game is tensed for career or not. Thats why we collected 804 data from them 780 were used the features we used are name, gender, age, spend time in study, sleep time, wake up time, the game play most ,spend time in playing games, purpose of playing games, spend time playing games more than family , time spend for skills, tensed for career. And then preprocessed them and Prior to using certain machine learning algorithms, they were examined. The accuracy of RF is best of 97.4% which outperformed all others.