Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Mousa, Ayad Hameed
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Food sales prediction model using machine learning techniques Merdas, Hussam Mezher; Mousa, Ayad Hameed
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6578-6585

Abstract

Food sales prediction means how to obtain future results of sales of companies. The purpose of this step is to increase the profits of these companies by avoiding spoilage of food products and avoiding buying more quantities than the needs of these companies, which means the accumulation of these products in the warehouses without selling them. Stocked and expired products require a model that guesses the actual future need for these products. In this study, a model for food sales prediction using machine learning algorithms is proposed to achieve two objectives, first: make a comparison between two datasets, one dataset with a high correlation between its features, and another dataset has a low correlation between its features. The second objective is to use several machine learning algorithms for prediction and comparing between these algorithms to find the best three algorithms that give the best prediction. By using the most important metrics such as root mean square error (RMSE) and mean square error (MSE) found the best three algorithms by using the first dataset are support vector machines (SVMs), least absolute shrinkage and selection operator (LASSO), and bagging regressor) and the best three algorithms by using the second dataset are (gradient boosting, random forest regressor, and decision tree).
Co-Authors Merdas, Hussam Mezher