Fuel Consumption Prediction: A Comparative Analysis of Machine Learning Paradigms

TL;DR

This study compares machine learning models for vehicle fuel consumption prediction, showing that classical models like SVM and Logistic Regression outperform deep learning in accuracy and interpretability, emphasizing physical design factors.

Contribution

It provides a comprehensive comparative analysis of ML paradigms for fuel prediction, highlighting the effectiveness of classical models over black-box deep learning architectures.

Findings

SVM Regression achieved R-squared of 0.889 and RMSE of 0.326.

Logistic Regression attained 90.8% accuracy and 0.957 recall.

Physical parameters like weight and displacement are key to vehicle efficiency.

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. These results challenge the current trend toward black-box deep learning architectures for static physical datasets, providing validation of robust performance by interpretable and well-tuned classical models. The research finds that intrinsic vehicle efficiency is fundamentally determined by physical design parameters, weight and displacement, offering a data-driven framework for how manufacturers should focus on lightweighting and engine downsizing to achieve stringent global sustainability goals.