Development of AI model to analyze the customer behavior by decision making system

Understanding the connection between the traits of consumers' mental processes and the rules of behaviour as they shop for and use goods and services is essential in consumer behaviour. E-commerce, or online shopping, has exploded in popularity along with the rise of smartphones and other mobile devices. Courses focusing on student happiness are essential for implementing experiential learning in E-commerce and consumer behaviour. This research suggests that online retailers use AI image recognition technologies to identify and interpret customers' facial emotions. To begin, it examines how human-computer interaction (HCI) works in electronic commerce and how HCI technology might increase product satisfaction among end users. Then, a deep neural network (DNN) is utilised to realise individualised product suggestions to anticipate customers' psychological behaviour and consumer psychology. Education on consumer behaviour helps comprehend customer happiness and create practical products. Consumers report high satisfaction with the items the system suggests, and the level of sanctification achieved in the experiments reaches 93.2%. The assessment shows that the DNN model can pick up on consumer behaviour patterns, and its prediction effect is ten per cent higher than that of the classic model, proving that the DNN model is a good fit for the recommendation system. Understanding customer happiness in E-commerce is greatly aided by this research since it gives a reference for HCI-based behavioural analysis of consumer psychology within the framework of AI.