Predicting Vehicle Fuel Efficiency: A Comparative Analysis of Machine Learning Models on the Auto MPG Dataset

Authors

DOI:

https://doi.org/10.5281/zenodo.10472864

Keywords:

MPG, Regression, Machine Learning

Abstract

This study explores the application of various machine learning models to predict vehicle fuel consumption using the Auto MPG dataset. It examines the effectiveness of algorithms such as Decision Tree Regressors, Random Forests, Support Vector Regressors, and neural network-based models like LSTM and GRU. The study aims to enhance fuel efficiency prediction by analyzing factors like engine specifications, driving habits, and vehicle design. The models' performance is evaluated using metrics such as R-squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) to ensure accuracy and minimize error.

Author Biographies

  • Alpay Doruk, Bandirma Onyedi Eylul University, Balıkesir, Turkey

    Dept. of Computer Engineering,
    Bandırma Onyedi Eylül University,
    Bandırma, Balıkesir, Turkey

  • Muhammed Ali Bayram, Bandirma Onyedi Eylul University, Balıkesir, Turkey

    Bandırma Onyedi Eylül University, Dept. of Computer Engineering, Bandırma, Balıkesir, Turkey

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Published

01-12-2023

Issue

Vol. 2 No. 2 (2023): Aintelia® Science Notes

Section

Articles

License

Copyright (c) 2023 AINTELIA Science Notes

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Predicting Vehicle Fuel Efficiency: A Comparative Analysis of Machine Learning Models on the Auto MPG Dataset. (2023). AINTELIA Science Notes Journal, 2(2), 1-11. https://doi.org/10.5281/zenodo.10472864