Transforming Retail Paradigms through Advanced AI-Enabled Autonomous Shopping Systems

The application of the Artificial Intelligence (AI) algorithms has been challenging in the retail industries One of the most intriguing advancements in retail is the advent of AI-powered automated stores, offering a seamless shopping journey for customers. These cutting-edge establishments integrate state-of-the-art technologies to revolutionize customer interactions, streamline operations, and eliminate the traditional checkout process. In this research paper, the Machine Learning (ML) algorithm, i.e., K-NN has been applied to classify three levels of satisfaction among the customer which are Unsatisfied, Neutral, and Satisfied. Here, we have applied the dataset of the customer behavior of Infosys Autonomous Store Simulation results to find that the proposed K-NN algorithm achieves high precision, F1-score, and Recall scores. This research contributes valuable insights of the proposed algorithm as a significant predictor of shopping intention, including perceived ease of use, usefulness, enjoyment, customization, and interactivity.