Machine learning vehicle fuel efficiency prediction

To address the challenges associated with fuel consumption in vehicles with low fuel efficiency, several factors must be recognized. Identifying the key factors of fuel efficiency prediction is crucial for making accurate decisions. Therefore, we propose a comprehensive framework that uses machine learning to predict fuel efficiency by integrating various vehicle information. The proposed method comprises a predictive model and analysis framework utilizing key vehicle attributes, such as fuel type, engine displacement, and vehicle grade, to enhance prediction accuracy. We conducted a comparative study using six machine-learning models. To evaluate the machine learning model, MSE (Mean Square Error), RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R-squared ([Formula: see text] Score) were used. We experimented with SHAP(Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and odds ratio analysis to evaluate the impact of various factors on fuel efficiency. We confirmed that the proposed method can predict fuel efficiency. Extra Trees Regressor and Random Forest Regressor demonstrated high prediction accuracy, particularly excelling in capturing nonlinear relationships. We also underscore the importance of identifying markers to support decision-making, offering critical insights into the key factors impacting fuel efficiency predictions.