A Novel Price Prediction Service for E-Commerce Categorical Data
-
Published:2023-04-20
Issue:8
Volume:11
Page:1938
-
ISSN:2227-7390
-
Container-title:Mathematics
-
language:en
-
Short-container-title:Mathematics
Author:
Fathalla Ahmed1ORCID, Salah Ahmad23ORCID, Ali Ahmed45ORCID
Affiliation:
1. Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt 2. Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt 3. College of Computing and Information Sciences, University of Technology and Applied Sciences, Ibri 516, Oman 4. Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia 5. Higher Future Institute for Specialized Technological Studies, Cairo 3044, Egypt
Abstract
Most e-commerce data include items that belong to different categories, e.g., product types on Amazon and eBay. The accurate prediction of an item’s price on an e-commerce platform will facilitate the maximization of economic benefits for the seller and buyer. Consequently, the task of price prediction of e-commerce items can be seen as a multiple regression on categorical data. Performing multiple regression tasks with categorical independent variables is tricky since the observations of each product type might have different distribution shapes, whereas the distribution shape of all the data might not be representative of each group. In this vein, we propose a service for facilitating the price prediction task of e-commerce categorical products. The main novelty of the proposed service relies on two unique data transformations aiming at increasing the between-group variance and decreasing the within-group variance to improve the task of regression analysis on categorical data. The proposed data transformations are tested on four different e-commerce datasets over a set of linear, non-linear, and neural network-based regression models. Comparing the best existing regression models without applying the proposed transformation, the proposed transformation results show improvements in the range of 1.98% to 8.91% for the four evaluation metrics scores, namely, R2, MAE, RMSE, and MAPE. However, the best metrics improvement on each dataset has average values of 16.8%, 8.0%, 6.0%, and 25.0% for R2, MAE, RMSE, and MAPE, respectively.
Funder
Prince Sattam Bin Abdulaziz University
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Reference34 articles.
1. Faiz, T., Aldmour, R., Ahmed, G., Alshurideh, M., and Paramaiah, C. (2023). The Effect of Information Technology on Business and Marketing Intelligence Systems, Springer. 2. Random-effects models for longitudinal data;Laird;Biometrics,1982 3. Vehicle price prediction system using machine learning techniques;Noor;Int. J. Comput. Appl.,2017 4. Yang, R.R., Chen, S., and Chou, E. (2018). AI Blue Book: Vehicle Price Prediction using Visual Features. arXiv. 5. Kalaiselvi, N., Aravind, K., Balaguru, S., and Vijayaragul, V. (2017, January 16–18). Retail price analytics using backpropogation neural network and sentimental analysis. Proceedings of the 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), Chennai, India.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Multimodal Approach for Painting Price Prediction;2023 5th International Conference on Cybernetics and Intelligent System (ICORIS);2023-10-06
|
|