Airline Choice: A Comparison of Classifiers in Traditional Analysis vs Decision Trees

Archana Shrivastava, P. James Daniel Paul and J.K. Sharma
Additional contact information
Archana Shrivastava: Amity School of Business, Amity University Uttarpradesh, Noida, India
P. James Daniel Paul: Ernst and Young LLP, Bengaluru, India
J.K. Sharma: Amity School of Business, Amity University Uttarpradesh, Noida, India

International Journal of Business Analytics (IJBAN), 2020, vol. 7, issue 2, 34-53

Abstract: Widespread use of e-commerce in the airline industry is generating data at unprecedented scale, thus rendering it amenable to decision analysis. Classification accuracy is one of the key factors in forecasting and in the decision sciences. The traditional classification analysis was carried out by several methods such as ANOVA, Logit, Probit. However, for decision analysis algorithms and decision trees have emerged for classification analysis. The objective of the article is to analyze the airline choice data using the traditional ANOVA and compare them with the decision trees and different algorithms.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJBAN.2020040103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jban00:v:7:y:2020:i:2:p:34-53

Access Statistics for this article

International Journal of Business Analytics (IJBAN) is currently edited by John Wang

More articles in International Journal of Business Analytics (IJBAN) from IGI Global
Bibliographic data for series maintained by Journal Editor ().