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Vehicle Price Classification and Prediction Using Machine Learning in the IoT Smart Manufacturing Era

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  • Fadi Al-Turjman

    (Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Nicosia 99138, Turkey
    Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Kyrenia 99320, Turkey)

  • Adedoyin A. Hussain

    (Computer Engineering Department, Research Centre for AI and IoT, Near East University, Mersin 10, Nicosia 99138, Turkey)

  • Sinem Alturjman

    (Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Nicosia 99138, Turkey
    Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Kyrenia 99320, Turkey)

  • Chadi Altrjman

    (Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Kyrenia 99320, Turkey
    Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

Abstract

In this paper, machine learning (ML) strategies have been utilized in predicting vehicles’ prices and good deals. Vehicle value prediction has been considered one of the most significant research topics with the rise of IoT for sustainability. This is because it requires observable exertion and massive field information. Towards generating a model that anticipates the vehicles’ price, we applied three ML methods (neural network, decision tree, support vector machine, and linear regression). However, the referenced methods have been applied to function together as a group in a hybrid model. The information utilized was gathered from an information and computer science school that houses different datasets. Separate exhibitions of several ML techniques were contrasted to reveal which one is suitable for the accessible information index. Various difficulties and challenges associated with this design have also been discussed. Moreover, the model was experimented, and a 90% precision was achieved. This potential result can help in providing precise vehicle deals in the emerging Internet of Things (IoT) for the sustainability paradigm.

Suggested Citation

  • Fadi Al-Turjman & Adedoyin A. Hussain & Sinem Alturjman & Chadi Altrjman, 2022. "Vehicle Price Classification and Prediction Using Machine Learning in the IoT Smart Manufacturing Era," Sustainability, MDPI, vol. 14(15), pages 1-11, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9147-:d:871925
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    References listed on IDEAS

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    1. Jie Du & Lili Xie & Stephan Schroeder, 2009. "—PIN Optimal Distribution of Auction Vehicles System: Applying Price Forecasting, Elasticity Estimation, and Genetic Algorithms to Used-Vehicle Distribution," Marketing Science, INFORMS, vol. 28(4), pages 637-644, 07-08.
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