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An Inquiry into Factors Responsible for Demand of Automobiles Using a Supervised Machine Learning Approach

Year: 2025

Type: article

Abstract: This study investigates the primary drivers of automobile demand using a supervised machine learning approach. Feature selection was performed using the Minimum Redundancy Maximum Relevance (mRMR) algorithm to identify the most influential variables. The analysis reveals that sedans (46.83%) and hatchbacks (34.15%) are the most preferred body styles, with a strong inclination toward vehicles featuring four doors (56.10%) and front-wheel drive (58.54%). Front-engine layouts dominate (98.54%), likely due to cost-efficiency and mechanical simplicity. These findings provide valuable insights for data-driven inventory and sales management, enabling automotive retailers to better align their offerings with consumer preferences. The study highlights the potential of machine learning to enhance decision-making and operational efficiency in the automotive sector.

Source: Formosa Journal of Science and Technology

DOI: 10.55927/fjst.v4i5.70

Authors: A.O Akinrotimi, J.O Atoyebi, O.O Owolabi, M.A Mabayoje

Institutions: Kings University, Adeleke University, University of Ilorin

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4411117410

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