using machine learning to predict service failure in khorasan razavi hotels

In today s competitive landscape, delivering quality services in hotels is crucial for ensuring customer satisfaction and achieving success in target markets. this paper explores the analysis and prediction of service failures in mashhad hotels through the application of machine learning techniques. the primary objective of this research is to identify and mitigate service failure, thereby enhancing the effectiveness of hotel marketing strategies. initially, data related to service failures across several hotels were gathered and analyzed. subsequently, a predictive model was developed using the decision tree method to forecast the likelihood of future failures. this model offers high accuracy in predicting potential service disruptions, enabling hotel management to implement proactive measures to improve service quality and customer satisfaction. furthermore, the simulation results indicate that employing this model can significantly reduce the rate of failures and contribute to the marketing strategies. ultimately, this research demonstrates that machine learning serves as a powerful tool for managing hotel services, positively impacting customer satisfaction and the financial performance of hotels.