Vehicle Price Classification and Prediction Using Machine Learning in the IoT Smart Manufacturing Era
Abstract
:1. Introduction
2. Literature Review
3. Methodology
- Data purifying and an assortment of notable datasets of the evaluations of car production.
- The method of ML is chosen.
- The model for vehicle prediction is obtained from the past information being handled.
- The acquired result is broken down and visualized.
3.1. Data Description
3.2. Machine Learning Method Selection
3.3. Experimental Approach
3.4. Computational Environment
4. Results
Models’ Accuracy
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Terms | Meaning |
ICT | Information and Communication Technologies |
ODAV | Optimal Distribution of Auction Vehicles |
ANN | Artificial Neural Network |
MF | Matrix Factorization |
ML | Machine Learning |
NN | Neural Networks |
LR | Linear Regression |
SL | Supervised Learning |
VS | Virtual Studio |
SVM | Support vector machine |
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Number | Buying | Maintenance | Door | Persons | Luggage Boot | Class |
---|---|---|---|---|---|---|
0 | Very High | Very High | 2 | 2 | Small | Unacceptable |
1 | Very High | Very High | 2 | 2 | Small | Unacceptable |
2 | Very High | Very High | 2 | 2 | Small | Unacceptable |
3 | Very High | Very High | 2 | 3 | Medium | Unacceptable |
4 | Very High | Very High | 2 | 2 | Medium | Unacceptable |
Models | Accuracy (%) | Error (%) |
---|---|---|
LR | 0.41 | 0.8 |
SVM | 0.49 | 0.10 |
DT | 0.45 | 0.6 |
NN | 0.42 | 0.7 |
Models | LR | SVM | DT | NN |
---|---|---|---|---|
Good/90% split | 0.81 | 0.86 | 0.85 | 0.83 |
10-fold cross-validation | 0.79 | 0.86 | 0.86 | 0.81 |
Very Good/90% split | 0.82 | 0.83 | 0.82 | 0.86 |
10-fold cross-validation | 0.76 | 0.78 | 0.75 | 0.79 |
Accepted/90% split | 0.84 | 0.90 | 0.88 | 0.85 |
10-fold cross-validation | 0.77 | 0.79 | 0.78 | 0.78 |
Unaccepted/90% split | 0.84 | 0.88 | 0.85 | 0.85 |
10-fold cross-validation | 0.78 | 0.82 | 0.81 | 0.81 |
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Al-Turjman, F.; Hussain, A.A.; Alturjman, S.; Altrjman, C. Vehicle Price Classification and Prediction Using Machine Learning in the IoT Smart Manufacturing Era. Sustainability 2022, 14, 9147. https://doi.org/10.3390/su14159147
Al-Turjman F, Hussain AA, Alturjman S, Altrjman C. Vehicle Price Classification and Prediction Using Machine Learning in the IoT Smart Manufacturing Era. Sustainability. 2022; 14(15):9147. https://doi.org/10.3390/su14159147
Chicago/Turabian StyleAl-Turjman, Fadi, Adedoyin A. Hussain, Sinem Alturjman, and Chadi Altrjman. 2022. "Vehicle Price Classification and Prediction Using Machine Learning in the IoT Smart Manufacturing Era" Sustainability 14, no. 15: 9147. https://doi.org/10.3390/su14159147