Comparing the Performance of Classification Algorithms for Predicting Electric Vehicle Adoption

Shamma AlRashdi (), Aysha AlHassani (), Fatima Haile (), Rauda AlNuaimi (), Thouraya Labben () and Gurdal Ertek ()
Additional contact information
Shamma AlRashdi: United Arab Emirates University
Aysha AlHassani: United Arab Emirates University
Fatima Haile: United Arab Emirates University
Rauda AlNuaimi: United Arab Emirates University
Thouraya Labben: United Arab Emirates University
Gurdal Ertek: United Arab Emirates University

Chapter Chapter 17 in Business Analytics and Decision Making in Practice, 2024, pp 203-214 from Springer

Abstract: Abstract In this study, the Electric Vehicle (EV) purchase decisions of European consumers are predicted using supervised machine learning (ML), specifically classification. Following the replacement (imputing) of missing data values through predicted values and continuizing of all predictor features, the predictor features are ranked according to the Information Gain Ratio and the Gini coefficient. The results suggest that suiting daily driving needs (Q17), belief that society must reward electric cars instead of petrol and diesel cars (Q14), and opinion change regarding electric cars during the past year (Q21) ranked the highest with respect to the Gini coefficient metric. The same predictor features rank the highest with respect to the Information Gain Ratio metric, yet in a different rank (Q17, Q21, and Q14). For predictive analytics, a multitude of classification algorithms are applied to predict the decision of EV purchase, and the performance of the applied algorithms is compared. The results suggest that gradient boosting performed best in predicting EV adoption decisions, followed by the logistic regression and random forest algorithms.

Keywords: Electric vehicles; Market adoption; Sustainable development goals (SDG); Machine learning; Feature ranking; Classification algorithms (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:lnopch:978-3-031-61589-4_17

Ordering information: This item can be ordered from
http://www.springer.com/9783031615894

DOI: 10.1007/978-3-031-61589-4_17

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().