Title: A data mining approach to predicting the inventory day of used cars

Authors: Dedy Suryadi; Alfian Tan; Donny Boy

Addresses: Industrial Engineering Department, Parahyangan Catholic University, Bandung, Indonesia ' Industrial Engineering Department, Parahyangan Catholic University, Bandung, Indonesia ' Industrial Engineering Department, Parahyangan Catholic University, Bandung, Indonesia

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%.

Keywords: data mining; decision making; prediction; feature selection; used car; inventory day.

DOI: 10.1504/IJKEDM.2021.119887

International Journal of Knowledge Engineering and Data Mining, 2021 Vol.7 No.1/2, pp.127 - 144

Received: 07 Mar 2021
Accepted: 08 Sep 2021
Published online: 22 Dec 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article