Analysis of factors affecting the future of the Game industry through Machine learning

The video game industry is a significant economic force, generating billions of dollars in revenue annually. This paper analyses the factors influencing consumer preferences for different types of video games, focusing on platform availability, pricing, game genres, and evolving market trends. Data was collected through a survey of over 250 respondents, capturing demographic attributes and gaming preferences. Machine learning techniques, including Decision Tree, Random Forest, and LightGBM, were applied to predict platform preferences and recommend game genres based on age. The Decision Tree model outperformed others with an accuracy of 99.98%, demonstrating its suitability for both tasks. This study provides actionable insights into consumer behaviour, emphasizing the role of brand reputation, marketing efforts, and franchise value in shaping game success. These findings aim to inform game developers and publishers in crafting personalized, data-driven strategies to enhance player engagement and satisfaction.