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Year 2024, Volume: 8 Issue: 1, 40 - 45, 31.07.2024

Abstract

References

  • [1] “Global Laptop Market Report and Forecast 2024-2032,” Laptop Market Share, Size, Trends, Growth, Analysis 2024-2032, https://www.expertmarketresearch.com/reports/laptop-market (accessed Oct. 30, 2023).
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  • [4] A. D. Siburian et al., “Laptop price prediction with machine learning using regression algorithm,” Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA), vol. 6, no. 1, pp. 87–91, 2022. doi:10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2850.
  • [5] M. A. Shaik, M. Varshith, S. SriVyshnavi, N. Sanjana and R. Sujith, "Laptop Price Prediction using Machine Learning Algorithms," 2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS), Nagpur, India, 2022, pp. 226-231, doi: 10.1109/ICETEMS56252.2022.10093357.
  • [6] A. A. Syed, Y. Heryadi, Lukas and A. Wibowo, “A Comparison of Machine Learning Classifiers on Laptop Products Classification Task,” Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS), Hong Kong, 2021.
  • [7] C. Ma et al., “Cost-sensitive deep forest for price prediction,” Pattern Recognition, vol. 107, p. 107499, 2020, doi:10.1016/j.patcog.2020.107499.
  • [8] I. M. Nasser, M. O. Al-Shawwa and S. S. Abu-Naser, “Developing Artificial Neural Network for Predicting Mobile Phone Price Range,” International Journal of Academic Information Systems Research (IJAISR), vol. 3, no. 2, pp. 1-6, 2019.
  • [9] M. A. Rahman, M. A. Kabir, M. E. Haque, and B. M. Hossain, “Machine learning-based price prediction for cows,” American Journal of Agricultural Science, Engineering, and Technology, vol. 5, no. 1, pp. 64–69, 2021, doi:10.54536/ajaset.v5i1.63.
  • [10] E. Güvenç, G. Çetin and H. Koçak, "Comparison of KNN and DNN Classifiers Performance in Predicting Mobile Phone Price Ranges", Advances in Artificial Intelligence Research, vol. 1, no. 1, pp. 19-28, Jan. 2021.
  • [11] E. Gegic, B. Isakovic, D. Keco, Z. Masetic and J. Kevric, “Car price prediction using machine learning techniques”, TEM Journal, vol. 8, no. 1, 2019, doi:10.18421/TEM81-16.
  • [12] S. Yücebaş, M. Doğan ve L. Genç, "A C4.5 – Cart Decision Tree Model For Real Estate Price Prediction And The Analysis of the Underlying Features", Konya Journal of Engineering Sciences, vol. 10, no. 1, pp. 147-161, 2022, doi:10.36306/konjes.1013833.
  • [13] M. Ortu, N. Uras, C. Conversano, S. Bartolucci, and G. Destefanis, “On technical trading and social media indicators for cryptocurrency price classification through Deep Learning,” Expert Systems with Applications, vol. 198, p. 116804, 2022. doi:10.1016/j.eswa.2022.116804.
  • [14] M. Varlı, “Laptop price”, Kaggle, https://www.kaggle.com/datasets/muhammetvarl/laptop-price (accessed Oct. 30, 2023).
  • [15] Tin Kam Ho, "Random decision forests," Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada, 1995, pp. 278-282 vol.1, doi: 10.1109/ICDAR.1995.598994.
  • [16] H. Nhat-Duc and T. Van-Duc, “Comparison of histogram-based gradient boosting classification machine, random forest, and deep convolutional neural network for Pavement Raveling Severity Classification,” Automation in Construction, vol. 148, p. 104767, 2023. doi:10.1016/j.autcon.2023.104767
  • [17] P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine Learning, vol. 63, no. 1, pp. 3–42, 2006. doi:10.1007/s10994-006-6226-1
  • [18] L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “CatBoost: unbiased boosting with categorical features,” arXiv (Cornell University), Jun. 2017, doi: 10.48550/arxiv.1706.09516.

Laptop Price Range Prediction with Machine Learning Methods

Year 2024, Volume: 8 Issue: 1, 40 - 45, 31.07.2024

Abstract

Prices forecasting, and price range estimation studies are very important for laptops, which have a very wide usage area, number of users and a large market share. Most existing price prediction studies use regression-based methods to estimate a concrete value for price. However, for many real-world applications, it is much more practical to predict a price class (or range). Although there are many studies on laptop price prediction in the literature, there is only one study on laptop price range prediction. The fact that the prices are divided into three different classes in this study does not overlap much with the laptop price range prediction problem in the real world. In addition, very few machine learning methods have been tested on the laptop price range prediction problem. To overcome these problems and contribute to the literature, a dataset previously used for laptop price prediction was adapted to be used for laptop price range prediction and the dataset was optimized for laptop price range prediction by applying preprocessing steps such as data cleaning, feature engineering and label encoding. Then, price range predictions were produced with machine learning methods such as random forest, histogram-based boosting, extra trees and catboost classifiers. When the success of the classifiers was tested, the best classifier was histogram-based boosting classifier with 70% accuracy.

References

  • [1] “Global Laptop Market Report and Forecast 2024-2032,” Laptop Market Share, Size, Trends, Growth, Analysis 2024-2032, https://www.expertmarketresearch.com/reports/laptop-market (accessed Oct. 30, 2023).
  • [2] Z. Youhan, “Machine learning can be divided into 3 categorizations: Supervised, unsupervised and reinforcement...,” Medium, https://zinayouhan33.medium.com/machine-learning-can-be-divided-into-3-categorizations-supervised-unsupervised-and-reinforcement-9a1b47460f5d (accessed Nov. 6, 2023).
  • [3] C. L. Reddy, K. B. Reddy, G. R. Anil, S. N. Mohanty and A. Basit, "Laptop Price Prediction Using Real Time Data," 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC), Jeddah, Saudi Arabia, 2023, pp. 1-5, doi: 10.1109/ICAISC56366.2023.10085473.
  • [4] A. D. Siburian et al., “Laptop price prediction with machine learning using regression algorithm,” Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA), vol. 6, no. 1, pp. 87–91, 2022. doi:10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2850.
  • [5] M. A. Shaik, M. Varshith, S. SriVyshnavi, N. Sanjana and R. Sujith, "Laptop Price Prediction using Machine Learning Algorithms," 2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS), Nagpur, India, 2022, pp. 226-231, doi: 10.1109/ICETEMS56252.2022.10093357.
  • [6] A. A. Syed, Y. Heryadi, Lukas and A. Wibowo, “A Comparison of Machine Learning Classifiers on Laptop Products Classification Task,” Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS), Hong Kong, 2021.
  • [7] C. Ma et al., “Cost-sensitive deep forest for price prediction,” Pattern Recognition, vol. 107, p. 107499, 2020, doi:10.1016/j.patcog.2020.107499.
  • [8] I. M. Nasser, M. O. Al-Shawwa and S. S. Abu-Naser, “Developing Artificial Neural Network for Predicting Mobile Phone Price Range,” International Journal of Academic Information Systems Research (IJAISR), vol. 3, no. 2, pp. 1-6, 2019.
  • [9] M. A. Rahman, M. A. Kabir, M. E. Haque, and B. M. Hossain, “Machine learning-based price prediction for cows,” American Journal of Agricultural Science, Engineering, and Technology, vol. 5, no. 1, pp. 64–69, 2021, doi:10.54536/ajaset.v5i1.63.
  • [10] E. Güvenç, G. Çetin and H. Koçak, "Comparison of KNN and DNN Classifiers Performance in Predicting Mobile Phone Price Ranges", Advances in Artificial Intelligence Research, vol. 1, no. 1, pp. 19-28, Jan. 2021.
  • [11] E. Gegic, B. Isakovic, D. Keco, Z. Masetic and J. Kevric, “Car price prediction using machine learning techniques”, TEM Journal, vol. 8, no. 1, 2019, doi:10.18421/TEM81-16.
  • [12] S. Yücebaş, M. Doğan ve L. Genç, "A C4.5 – Cart Decision Tree Model For Real Estate Price Prediction And The Analysis of the Underlying Features", Konya Journal of Engineering Sciences, vol. 10, no. 1, pp. 147-161, 2022, doi:10.36306/konjes.1013833.
  • [13] M. Ortu, N. Uras, C. Conversano, S. Bartolucci, and G. Destefanis, “On technical trading and social media indicators for cryptocurrency price classification through Deep Learning,” Expert Systems with Applications, vol. 198, p. 116804, 2022. doi:10.1016/j.eswa.2022.116804.
  • [14] M. Varlı, “Laptop price”, Kaggle, https://www.kaggle.com/datasets/muhammetvarl/laptop-price (accessed Oct. 30, 2023).
  • [15] Tin Kam Ho, "Random decision forests," Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada, 1995, pp. 278-282 vol.1, doi: 10.1109/ICDAR.1995.598994.
  • [16] H. Nhat-Duc and T. Van-Duc, “Comparison of histogram-based gradient boosting classification machine, random forest, and deep convolutional neural network for Pavement Raveling Severity Classification,” Automation in Construction, vol. 148, p. 104767, 2023. doi:10.1016/j.autcon.2023.104767
  • [17] P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine Learning, vol. 63, no. 1, pp. 3–42, 2006. doi:10.1007/s10994-006-6226-1
  • [18] L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “CatBoost: unbiased boosting with categorical features,” arXiv (Cornell University), Jun. 2017, doi: 10.48550/arxiv.1706.09516.
There are 18 citations in total.

Details

Primary Language English
Subjects Machine Learning (Other), Data Mining and Knowledge Discovery
Journal Section Articles
Authors

Yasin Karakuş 0000-0002-4534-0151

Turgay Tugay Bilgin 0000-0002-9245-5728

Early Pub Date July 9, 2024
Publication Date July 31, 2024
Submission Date January 7, 2024
Acceptance Date July 1, 2024
Published in Issue Year 2024 Volume: 8 Issue: 1

Cite

IEEE Y. Karakuş and T. T. Bilgin, “Laptop Price Range Prediction with Machine Learning Methods”, IJMSIT, vol. 8, no. 1, pp. 40–45, 2024.