Performance of Machine Learning Models to Predict Customer Satisfaction Scores for Lao National Convention Center

This research paper investigates the effectiveness of various machine learning (ML) models in predicting customer satisfaction scores for the Lao National Convention Center (LNCC). By utilizing a dataset containing customer feedback, demographic data, facilities, and service-related information, the study aims to identify the best performing model for predicting customer satisfaction. In this paper, the performance of ML to predict customer satisfaction scores from the questionnaire survey dataset is evaluated. The customer satisfaction score is categorized into five classes: strongly agree, agree, neutral, disagree and strongly disagree. The analysis will compare the performance of different models, including linear regression (LR), k-nearest neighbor (KNN), support vector machines (SVM), decision trees (DT) and random forest (RF). The results show that SVM model achieved the highest accuracy rate of 95.91%, followed by KNN and LR with an accuracy rate of 95.55% and 95.49%, respectively. The findings of this study have important implications for the use of ML in improving the service-related information for LNCC and providing valuable insights for decision-makers and developers.