Naive Bayes and KNN for Airline Passenger Satisfaction Classification: Comparative Analysis
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Abstract
Air transportation is vital due to technological advancements and globalization. It is affordable and accessible worldwide, providing efficient services to reach destinations globally. This discussion focuses on full-service airlines that offer online-based services. Previous research indicates that available facilities and services influence passenger satisfaction. Previous research on customer satisfaction showed a correlation between satisfaction and services without accurate figures. In the present study, the customer satisfaction figure is measured using the Naive Bayes and K-Nearest Neighbour (K-NN) algorithm to obtain a tested level of accuracy. In this analysis, we will compare the effectiveness of Naive Bayes and K-NN algorithms in classifying airline passenger satisfaction. The results show that the accuracy of the Naive Bayes method of the two algorithms is higher than the K-NN method. The accuracy value of the Naive Bayes method is 84.48%, while the accuracy value of the K-NN method is 65.38%. From the test results, the precision value for Naive Bayes is 82.25%, and K-NN is 67.35%. Furthermore, the recall value for Naive Bayes is 82.43%, and K-NN is 74.33%.
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