A data mining approach to predicting the inventory day of used cars. Issue 1 (9th December 2021)

• Record Type: Journal Article
• Authors: Suryadi, Dedy; Tan, Alfian; Boy, Donny
• Is Part Of: International journal of knowledge engineering and data mining. Volume 7:Issue 1/2(2020)
• Page Start: 127
• Page End: 144
• Publication Date: 2021-12-09
• Subjects: data mining -- decision making -- prediction -- feature selection -- used car -- inventory day; Knowledge representation (Information theory) -- Periodicals; Data mining -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-2087

Abstract

This paper studies the decision-making process in purchasing used cars at a company. The company's main objective is to purchase cars that may be sold within 30 days. Currently, the decision is solely made based on the subjective judgment of a supervisor. Alternatively, utilising the data that has been collected by the company, a data mining approach is proposed to improve the decision-making process. Out of the 45 aspects of a car, 12 features are selected as being important using the contingency table method. Six data mining methods are applied. Support vector machine (SVM) prediction model performs the best. The SVM model provides an accuracy of 69.44% in predicting whether or not a used car would be successfully sold within the acceptable inventory days, i.e., 30 days. In contrast, the predictive accuracy of the current decision-making process is just around 50%.