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

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.