A comparative study of statistical and intelligent classification models for predicting airlines passenger management satisfaction
This paper compares Statistical and Intelligent classification models in predicting passenger satisfaction with airlines. It seeks to identify the most accurate and reliable model amongst GLM, Robust Regression, MARS, KNN and Neural Networks. Methodology. These models were analyzed using a set of pe...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825001358 |
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| author | Mohammed Alharithi Ehab M. Almetwally Omar Alotaibi Marwa M. Eid El-Sayed M. El-kenawy Alaa A. Elnazer |
| author_facet | Mohammed Alharithi Ehab M. Almetwally Omar Alotaibi Marwa M. Eid El-Sayed M. El-kenawy Alaa A. Elnazer |
| author_sort | Mohammed Alharithi |
| collection | DOAJ |
| description | This paper compares Statistical and Intelligent classification models in predicting passenger satisfaction with airlines. It seeks to identify the most accurate and reliable model amongst GLM, Robust Regression, MARS, KNN and Neural Networks. Methodology. These models were analyzed using a set of performance metrics: MSE, MAE, R-squared, RMSE, COV, CC, EC, and overall accuracy. Data set comes from KaGGle website. The results obtained from these models were analyzed with the help of descriptive statistics, statistical tests of ANOVA, and the Wilcoxon signed rank test to establish significant differences in the performance of these models. Key results are that MARS is significantly better than others, with the least MSE (0.0717), MAE (0.0717), RMSE (0.2679) and the highest R² (0.7078) and CC (0.8537) among them. The ANOVA of the test results showed significant differences between the models, as the P-value calculated was less than 0.0001. This confirmed that the performance variations obtained are of statistical significance. Similar results are confirmed with the Wilcoxon Signed Rank Test, with P-values of 0.002 for all models. This shows significant differences between the theoretical and actual medians. Conclusions. MARS is the most effective model for predicting passenger satisfaction with Airlines as it is more accurate and reliable. The critical potential research domain is to develop Hybrid models by blending the best of multiple approaches for better accuracy and robustness in passenger satisfaction with airline prediction. |
| format | Article |
| id | doaj-art-10b30c7091de485badd487c66beae8ef |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-10b30c7091de485badd487c66beae8ef2025-02-02T05:26:53ZengElsevierAlexandria Engineering Journal1110-01682025-04-0111999110A comparative study of statistical and intelligent classification models for predicting airlines passenger management satisfactionMohammed Alharithi0Ehab M. Almetwally1Omar Alotaibi2Marwa M. Eid3El-Sayed M. El-kenawy4Alaa A. Elnazer5Department of Management, College of Business Administration in Hawtat Bani Tamim, Prince Sattam Bin Abdul Aziz University, Saudi ArabiaDepartment of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11564, Saudi Arabia; Corresponding author.Department of Accounting, College of Business Administration in Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Saudi ArabiaFaculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, EgyptSchool of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain; Applied Science Research Center. Applied Science Private University, Amman, Jordan; Jadara University Research Center, Jadara University, Jordan; Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, EgyptFaculty of Business Administration, Delta University for Science and Technology, Mansoura, Gamasa, EgyptThis paper compares Statistical and Intelligent classification models in predicting passenger satisfaction with airlines. It seeks to identify the most accurate and reliable model amongst GLM, Robust Regression, MARS, KNN and Neural Networks. Methodology. These models were analyzed using a set of performance metrics: MSE, MAE, R-squared, RMSE, COV, CC, EC, and overall accuracy. Data set comes from KaGGle website. The results obtained from these models were analyzed with the help of descriptive statistics, statistical tests of ANOVA, and the Wilcoxon signed rank test to establish significant differences in the performance of these models. Key results are that MARS is significantly better than others, with the least MSE (0.0717), MAE (0.0717), RMSE (0.2679) and the highest R² (0.7078) and CC (0.8537) among them. The ANOVA of the test results showed significant differences between the models, as the P-value calculated was less than 0.0001. This confirmed that the performance variations obtained are of statistical significance. Similar results are confirmed with the Wilcoxon Signed Rank Test, with P-values of 0.002 for all models. This shows significant differences between the theoretical and actual medians. Conclusions. MARS is the most effective model for predicting passenger satisfaction with Airlines as it is more accurate and reliable. The critical potential research domain is to develop Hybrid models by blending the best of multiple approaches for better accuracy and robustness in passenger satisfaction with airline prediction.http://www.sciencedirect.com/science/article/pii/S1110016825001358Airline passenger satisfactionPredictive modelingLogistic regressionK-Nearest Neighbor (KNN)Neural networksMachine learning regression |
| spellingShingle | Mohammed Alharithi Ehab M. Almetwally Omar Alotaibi Marwa M. Eid El-Sayed M. El-kenawy Alaa A. Elnazer A comparative study of statistical and intelligent classification models for predicting airlines passenger management satisfaction Alexandria Engineering Journal Airline passenger satisfaction Predictive modeling Logistic regression K-Nearest Neighbor (KNN) Neural networks Machine learning regression |
| title | A comparative study of statistical and intelligent classification models for predicting airlines passenger management satisfaction |
| title_full | A comparative study of statistical and intelligent classification models for predicting airlines passenger management satisfaction |
| title_fullStr | A comparative study of statistical and intelligent classification models for predicting airlines passenger management satisfaction |
| title_full_unstemmed | A comparative study of statistical and intelligent classification models for predicting airlines passenger management satisfaction |
| title_short | A comparative study of statistical and intelligent classification models for predicting airlines passenger management satisfaction |
| title_sort | comparative study of statistical and intelligent classification models for predicting airlines passenger management satisfaction |
| topic | Airline passenger satisfaction Predictive modeling Logistic regression K-Nearest Neighbor (KNN) Neural networks Machine learning regression |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825001358 |
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