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

Fadi Al-Turjman, Adedoyin A. Hussain, Sinem Alturjman and Chadi Altrjman
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Fadi Al-Turjman: Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Nicosia 99138, 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
Chadi Altrjman: Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Kyrenia 99320, Turkey

Sustainability, 2022, vol. 14, issue 15, 1-11

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.

Keywords: car sales; sustainability; machine learning; support vector machine; linear regression; neural networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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