A Novel Price Prediction Service for E-Commerce Categorical Data

Ahmed Fathalla, Ahmad Salah () and Ahmed Ali
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Ahmed Fathalla: Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt
Ahmad Salah: Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
Ahmed Ali: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

Mathematics, 2023, vol. 11, issue 8, 1-20

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, R 2 , 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 R 2 , MAE, RMSE, and MAPE, respectively.

Keywords: between-group variance; categorical data; data transformation; E-commerce; price prediction; within-group variance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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