<![CDATA[This research aims to classify customer feedback from Restaurant X using the LightGBM model to enhance service quality and customer satisfaction amidst growing industry competition. Customer feedback, collected through surveys and online platforms, is analyzed to uncover patterns and trends related to various aspects of the dining experience. The methodology encompasses data collection, preprocessing, model training, and evaluation. LightGBM, renowned for its efficiency and accuracy with large datasets, serves as the primary tool for building a robust classification model. Analysis reveals that key features such as food quality, service, and cleanliness significantly influence customer satisfaction. The model demonstrates high classification accuracy, providing actionable insights for Restaurant X management. These insights enable targeted strategies for improving specific areas of service, fostering better customer experiences and driving loyalty. The research underscores the importance of leveraging advanced machine learning models like LightGBM for data-driven decision-making in the restaurant industry.]]>
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