Volume 3 , Issue 2 , PP: 40-48, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Abedallah Z. Abualkishik 1 * , Rasha Almajed 2
In the field of Operations Research, the growing popularity of fruits, avocados, in the United States has sparked a need for thorough market analysis. This study aims to use Agent Based Modeling (ABM) principles to understand and predict sales volumes. By using intelligence techniques, the Extra Trees Regressor (ETR) we strive to identify the various factors that influence avocado sales. Our approach involves modeling data within ABM to provide an assessment and comparison, with classifiers. The results clearly demonstrate that ETR outperforms classifiers when it comes to predicting sales volume. Through plots and error prediction curves we can see how this model effectively captures sales patterns in a dynamic market environment. The predictive prowess of the proposed solution is validated through visual evaluation tools including residual plots as well as prediction curves, which prove its adeptness in predicting operational sales patterns within a dynamic market. The findings of our experiments study put emphasis on role of intelligence-based Agent-Based Modeling within Operations Research, exemplified by the Extra Trees Regressor, which offer a reliable tool for elucidating and projecting intricate market trends.
Business Intelligence , US sales volume trends , Market dynamics modeling , Machine learning , Consumer preferences analysis , big data analysis , Market trend forecasting.
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