Sports Game Classification and Detection Using ResNet50 Model Through Machine Learning Techniques Using Artificial Intelligence

The intention of artificial intelligence (AI) sports analysis is to use AI and machine learning to get useful insights and enhance sports performance. Data from sporting events, players, and welfare equipment are processed and analysed using AI methods. Machine learning, computer vision, natural language processing, and other forms of artificial intelligence analysis are used to improve every facet of sports, from player efficiency to audience participation. It's a fast-paced, ever-changing industry that's revolutionising the way athletes, coaches, and fans interact with the sporting world. The ResNet-50 model and other machine learning techniques are at the bleeding edge of research into sports analysis and performance enhancement. The deep learning model ResNet-50 is used to analyse video and stills from sporting events. Application areas include player monitoring, injury prediction, and game result predicting, all of which are made possible by the use of transfer learning to collect subtle details in sports data. This approach ushers in a new age of data-driven decision making in the sports industry while also revolutionising sports analytics, providing access to invaluable information for coaches, players, and spectators. The primary objective of this research was to categorise nine distinct sports-related picture classifications and predict their expected accuracy, which came in at 93 percent. Future researchers in this field of study will benefit from the high level of accuracy shown in this work.