A Hybrid Approach for Product Price Prediction

Rola M. Elbakly, Magda M. Madbouly and Shawkat K. Guirguis
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Rola M. Elbakly: Institute of Graduate Studies and Research, Egypt.
Magda M. Madbouly: Institute of Graduate Studies and Research, Egypt.
Shawkat K. Guirguis: Institute of Graduate Studies and Research, Egypt.

European Journal of Engineering and Technology Research, 2022, vol. 7, issue 5, 32-36

Abstract: In alignment with today’s online market needs, this study is concerned with a major topic present in any purchasing interaction, namely price prediction. It is of critical importance to both the buyer and seller to be able to estimate the proper price of the prospective merchandise with accuracy to ensure maximum profit and avoid any possible fraud situations. The purpose of our work is to test the deep learning network used in previous literature to predict prices from image data only, on an image data set of a more complex application, namely real estate listings. Also, a hybrid model is designed to improve price prediction accuracy by combining both numerical and image data predictions. The proposed model has achieved a Mean Squared Logarithmic Error (MSLE) of 0.05 and a RÇ of 0.91.

Keywords: Deep Neural Networks; Price Prediction; Regression. (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:7:y:2022:i:5:id:62883

DOI: 10.24018/ejeng.2022.7.5.2883

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