Neural Network for Predicting Dining Experiences at Restaurant X

Abstract

This study explores the application of neural networks to predict dining experiences at Restaurant X, utilizing a combination of customer feedback, operational data, and sales transactions. The goal is to enhance restaurant management through accurate predictions of customer satisfaction and operational performance. Customer reviews, sentiment analysis, and operational data were processed using natural language processing (NLP) and time-series analysis to prepare the data for neural network training. The model's performance was evaluated using metrics such as accuracy, precision, recall, and F1-score, and it was compared with traditional machine learning techniques like logistic regression and decision trees. The results demonstrate that neural networks outperform traditional algorithms in predicting customer sentiment and dining experiences. This study highlights the potential of deep learning to provide valuable insights into customer behavior, enabling restaurants to improve service personalization, marketing strategies, and operational efficiency. Future research can focus on expanding the dataset and exploring more advanced deep learning models to further enhance prediction accuracy and applicability in the hospitality industry.