[2603.21034] Fuel Consumption Prediction: A Comparative Analysis of Machine Learning Paradigms

Computer Science > Machine Learning arXiv:2603.21034 (cs) [Submitted on 22 Mar 2026] Title:Fuel Consumption Prediction: A Comparative Analysis of Machine Learning Paradigms Authors:Ali Akram View a PDF of the paper titled Fuel Consumption Prediction: A Comparative Analysis of Machine Learning Paradigms, by Ali Akram View PDF Abstract:The automotive industry is under growing pressure to reduce its environmental impact, requiring accurate predictive modeling to support sustainable engineering design. This study examines the factors that determine vehicle fuel consumption from the seminal Motor Trend dataset, identifying the governing physical factors of efficiency through rigorous quantitative analysis. Methodologically, the research uses data sanitization, statistical outlier elimination, and in-depth Exploratory Data Analysis (EDA) to curb the occurrence of multicollinearity between powertrain features. A comparative analysis of machine learning paradigms including Multiple Linear Regression, Support Vector Machines (SVM), and Logistic Regression was carried out to assess predictive efficacy. Findings indicate that SVM Regression is most accurate on continuous prediction (R-squared = 0.889, RMSE = 0.326), and is effective in capturing the non-linear relationships between vehicle mass and engine displacement. In parallel, Logistic Regression proved superior for classification (Accuracy = 90.8%) and showed exceptional recall (0.957) when identifying low-efficiency vehicles. ...