<![CDATA[In the highly competitive era of the modern hospitality industry, service quality, cleanliness, and the completeness of facilities are the main pillars in maintaining guest loyalty and business reputation. Hotel NICI, as one of the accommodation service providers, faces challenges in extracting valuable insights from the accumulation of large-volume guest feedback survey data. This study aims to systematically classify the level of guest satisfaction with Hotel NICI's services using a Decision Tree machine learning algorithm based on survey data. The dataset used covers various evaluation dimensions, particularly guests' perceptions of staff service quality, the cleanliness level of hotel areas, and the functionality of the facilities provided. To produce a valid model, the raw data first undergoes a series of rigorous preprocessing stages. These stages include handling missing values using imputation methods, normalizing nomenclature or column names for data consistency, as well as[1] converting qualitative textual responses into structured categories, namely 'satisfied', 'fairly satisfied', and 'dissatisfied'. To determine the global sentiment label, a majority voting approach is applied to consolidate the three satisfaction dimensions into a single target variable representing the overall guest satisfaction category. Subsequently, these categorical features along with the target variable are numerically encoded using the label encoding technique, then divided into training data (training set) and testing data (testing set) with a proportional ratio of 80:20. The Decision Tree Classifier model is trained by optimizing the tree depth parameter to prevent overfitting.]]>
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