Predicting consumer acceptance of automobiles based on deep learning and traditional machine learning algorithms

Research Article
Open access

Predicting consumer acceptance of automobiles based on deep learning and traditional machine learning algorithms

Linrang Yang 1*
  • 1 University of Alberta    
  • *corresponding author linrang@ualberta.ca
Published on 11 December 2023 | https://doi.org/10.54254/2755-2721/27/20230119
ACE Vol.27
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-199-5
ISBN (Online): 978-1-83558-200-8

Abstract

Researchers have made significant progress in machine learning in recent years. Machine learning can learn and predict large and complex data sets. Researchers have divided machine learning algorithms into two categories: deep learning and traditional machine learning. Every problem can be predicted in both ways. This paper uses the "Car Data" dataset to investigate deep learning and traditional machine learning. In order to find a machine learning algorithm that is more conducive to analyzing and predicting consumers' acceptance of different cars, this paper mainly explores the differences in the prediction accuracy of the three methods of Neural Networks, Random Forest and Support Vector Machine (SVM). We construct 3-hidden layers neural networks and 4-hidden layers neural networks. After testing, it is known that the result predicted by Random Forest is the worst. The prediction accuracy of 3-hidden layers Neural Networks is similar to that by SVM. When we added an extra layer of hidden layers on the basis of 3-hidden layers, the prediction accuracy was higher than that of SVM. Adding a hidden layer can improve the prediction accuracy, and both SVM and Neural Network can be used to analyze Car Data. But not all methods have similar predictive accuracy.

Keywords:

Deep Learning, Machine Learning, Neural Network, Random Forest, SVM

Yang,L. (2023). Predicting consumer acceptance of automobiles based on deep learning and traditional machine learning algorithms. Applied and Computational Engineering,27,30-37.
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References

[1]. John Healy, Leland McInnes, Colin Weir. Bridging the Cyber-Analysis Gap: The Democratization of Data Science, The Cyber Defense Review, 2017, Vol. 2, No. 1:109-118

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[4]. Chengyu Xie, Lei Chao, Dongping Shi, Zhou Ni. Evaluation of Sustainable Use of Water Resources Based on Random Forest, Journal of Coastal Research, WINTER 2020: 134-136

[5]. Joe P, Melo S, Burrows W, Casati B, Crawford R, Deghan A, Gascon G, Mariani Z, Milbrandt J, Strawbridge K. Supersite at Iqaluit, Bulletin of the American Meteorological Society , April 2020, Vol. 101: 305-312

[6]. R Eslami, M Azarnoush, A Kialashki and F Kazemzadeh. GIS-BASED FOREST FIRE SUSCEPTIBILITY ASSESSMENT BY RANDOM FOREST, ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION METHODS, Journal of Tropical Forest Science, April 2021, Vol. 33, No. 2: 173-184.

[7]. Richard Berk, Jordan Hyatt. Machine Learning Forecasts of Risk to Inform Sentencing Decisions, Federal Sentencing Reporter, Vol. 27, No. 4: 222-228.

[8]. Zhijian Liu, Di Wu, Yuanwei Liu, Zhonghe Han, Liyong Lun, Jun Gao, Guangya, Jin, Guoqing Cao. Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction, Energy Exploration & Exploitation, July 2019, Vol. 37, No. 4: 1426-1451.

[9]. Nico Orlandi. Predictive perceptual systems, Synthese, Vol. 195, No. 6: 2367-2386

[10]. Daniel R. Gambill, Wade A. Wall, Andrew J. Fulton, Heidi R. Howard. Predicting USCS soil classification from soil property variables using Random Forest, Journal of Terramechanics 65 (2016) : 85–92.

[11]. Data Source, Car Evaluation Data Set, Retrieved from: https://archive.ics.uci.edu/ml/datasets/Car+Evaluation

Cite this article

Yang,L. (2023). Predicting consumer acceptance of automobiles based on deep learning and traditional machine learning algorithms. Applied and Computational Engineering,27,30-37.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-83558-199-5(Print) / 978-1-83558-200-8(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.27
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. John Healy, Leland McInnes, Colin Weir. Bridging the Cyber-Analysis Gap: The Democratization of Data Science, The Cyber Defense Review, 2017, Vol. 2, No. 1:109-118

[2]. Benjamin C. Jantzen. Discovery without a miracle, Synthese, October 2016, Vol. 193, No. 10: 3209-3238

[3]. Mike Ananny.Toward an Ethics of Algorithms: Convening, Observation, Probability, and Timeliness, Science, Technology, & Human Values, January 2016, Vol. 41, No. 1: 93-117

[4]. Chengyu Xie, Lei Chao, Dongping Shi, Zhou Ni. Evaluation of Sustainable Use of Water Resources Based on Random Forest, Journal of Coastal Research, WINTER 2020: 134-136

[5]. Joe P, Melo S, Burrows W, Casati B, Crawford R, Deghan A, Gascon G, Mariani Z, Milbrandt J, Strawbridge K. Supersite at Iqaluit, Bulletin of the American Meteorological Society , April 2020, Vol. 101: 305-312

[6]. R Eslami, M Azarnoush, A Kialashki and F Kazemzadeh. GIS-BASED FOREST FIRE SUSCEPTIBILITY ASSESSMENT BY RANDOM FOREST, ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION METHODS, Journal of Tropical Forest Science, April 2021, Vol. 33, No. 2: 173-184.

[7]. Richard Berk, Jordan Hyatt. Machine Learning Forecasts of Risk to Inform Sentencing Decisions, Federal Sentencing Reporter, Vol. 27, No. 4: 222-228.

[8]. Zhijian Liu, Di Wu, Yuanwei Liu, Zhonghe Han, Liyong Lun, Jun Gao, Guangya, Jin, Guoqing Cao. Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction, Energy Exploration & Exploitation, July 2019, Vol. 37, No. 4: 1426-1451.

[9]. Nico Orlandi. Predictive perceptual systems, Synthese, Vol. 195, No. 6: 2367-2386

[10]. Daniel R. Gambill, Wade A. Wall, Andrew J. Fulton, Heidi R. Howard. Predicting USCS soil classification from soil property variables using Random Forest, Journal of Terramechanics 65 (2016) : 85–92.

[11]. Data Source, Car Evaluation Data Set, Retrieved from: https://archive.ics.uci.edu/ml/datasets/Car+Evaluation

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