Enhancing Customer Service Efficiency in the Holy Makkah Municipality Using Machine Learning

This study investigates the application of Machine Learning to enhance customer service efficiency within the Holy Makkah Municipality, focusing on the Tasahalat service. By analyzing historical data, we developed a model designed to automatically classify customer inquiries and deliver prompt, automated responses. Several Machine Learning algorithms, including Support Vector Machines, Naive Bayes, and Random Forest, were employed in the process. The research emphasizes the superior performance of the Random Forest algorithm, which achieved an impressive accuracy of 93% and a robust F1 score. By utilizing Natural Language Processing techniques to handle Arabic-language data, the model substantially improves response times and service quality, leading to enhanced customer satisfaction and more efficient municipal operations. This research underscores the potential of integrating advanced Machine Learning technologies into public services, setting the stage for future innovations in the sector.