Evaluating Service Quality Metrics with AdaBoost Classifier at Restaurant X

Abstract

This paper explores the use of the AdaBoost classifier to evaluate service quality metrics in the restaurant industry, specifically at Restaurant X. The study focuses on how machine learning, particularly ensemble learning algorithms, can improve the understanding of customer satisfaction by analyzing various service attributes, such as food quality, staff behavior, wait times, and ambiance. By applying AdaBoost, the model combines multiple weak classifiers to create a stronger, more accurate prediction model that identifies key factors influencing customer experience. The research highlights the importance of real-time data and customer feedback in refining service quality metrics and suggests that incorporating sentiment analysis and other dynamic data sources can provide a more comprehensive view of customer satisfaction. The findings suggest that using machine learning algorithms, like AdaBoost, can enhance operational decision-making, improve customer service, and contribute to overall business success. Additionally, the study proposes the continuous updating of the model to reflect changing customer preferences and trends in the competitive food service industry. This approach can lead to better service, customer retention, and a strategic advantage for restaurants seeking to meet the evolving demands of the market.