Earticle

초록

영어

There are many airline customer evaluation data, but they are insufficient in terms of predicting customer satisfaction in practice. In particular, they are generally insufficient in case of verification of data value and development of a customer satisfaction prediction model based on customer evaluation data. In this paper, airline customer satisfaction analysis is conducted through an experiment of correlation analysis between customer evaluation data provided by Google's Kaggle. The difference in accuracy varied according to the three types, which are the overall variables, the top 4 and top 8 variables with the highest correlation. To build an airline customer satisfaction prediction model, they are applied to three classification algorithms of Random Forest, SVM, DNN and conduct a classification experiment. They are divided into training data and verification data by 7:3. As a result, the DNN model showed the lowest accuracy at 86.4%, while the SVM model at 89% and the Random Forest model at 95.7% showed the highest accuracy and performance.

목차

Abstract
1. Introduction
2. Related Literature
3. Experimental Process
3.1 Airline customer evaluation data
3.2 Data preprocessing
3.3 classification algorithm
4. Evaluation and Discussion
5. Conclusion
References

키워드

저자

• Sang Hoon Hong [ Student, Dept. of Medical IT, Eulji University, Korea ]
• Bumsu Kim [ Director, Div. of Customer &Media, Korea Telecomm, Korea ]
• Yong Gyu Jung [ Professor, Dept. of Medical IT, Eulji University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380