International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 13, No. 1, 8 Feb. 2021
Cover page and Table of Contents: PDF (size: 891KB)
PriceCop – Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform
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Author(s)
Mohamed Zaim Shahrel
1,*
Sofianita Mutalib
1
Shuzlina Abdul Rahman
1
Index Terms
Price Prediction, Linear Regression, Least Squares Support Vector Machine (LSSVM), Artificial Bee Colony (ABC).
Abstract
In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the “discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers’ monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day; thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year’s worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.
Cite This Paper
Mohamed Zaim Shahrel, Sofianita Mutalib, Shuzlina Abdul-Rahman, "PriceCop–Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.13, No.1, pp. 1-14, 2021. DOI:10.5815/ijieeb.2021.01.01
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