A Machine Learning Based Approach to Predict the Effects of Video Games on Youth Behavior

Video game addiction is a rapidly growing concern among young people, emerging as a major issue due to its harmful impact on their mental health. This study investigates how predictive models might reveal subtle patterns in gaming habits by examining the relationship between video games, youth development, and machine learning. The research will investigate social dynamics as well as cognitive, academic, and mental health implications in order to educate individuals about responsible gaming habits and teaching tactics. The study covers ethical considerations and long-term societal ramifications, emphasizing the significance of a compatible way to navigating the IT based world because of young people's prosperity. Its purpose is to evaluate whether a gamer is career-minded. That's why we gathered 804 data points from them, of which 780 were utilized. The key point that we utilized were name, gender, age, amount of time spent studying, amount of sleep or wakefulness, amount of time spent playing games, reason for playing games, amount of time spent playing games more than with family, time spent honing skills, and time spent worried about a career. and after that, they underwent reprocessing and were checked before being used with certain machine learning algorithms. Various prediction and find ways use machine learning, artificial intelligence, and deep learning method. Our approaches include Gaussian Naive Bayes (GNB), Random Forest (RF), Adaptive Boosting (ADA Boosting), Decision Tree (DT), Extra Trees (ET), Gradient Boosting (GB) classifier, KNN (K-Nearest Neighbors), Support Vector Machine (SVM). The Random Forest classifier performed better in our analysis than the other eight methods. While all eight classifiers have great prediction accuracy, Random Forest Classifier (RF) has the highest accuracy of 97.00 %. This study analyses the various behaviours or habits of the current youth society. Based on the results obtained from that analysis, a machine learning-based model has been proposed. This proposed model will help in understanding the behavior of young people and their current situation.