Department of Information Systems and Operations Management United States Of America, University of Texas at Arlington.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 1958-1975
Article DOI: 10.30574/wjarr.2024.23.2.2540
Received on 08 July 2024; revised on 20 August 2024; accepted on 22 August 2024
This research paper explores the integration of reinforcement learning (RL) into data analysis, contrasting it with traditional methods. As the role of data analysts becomes increasingly crucial in decision-making processes across industries, the need for more sophisticated tools and approaches has grown. Reinforcement learning, a subset of machine learning, offers a promising avenue for enhancing decision-making by enabling systems to learn optimal strategies through trial and error. This paper examines the theoretical foundations of reinforcement learning, its applications in data analysis, and compares its effectiveness against traditional methods. We conclude by discussing the future implications of RL in data analysis and the potential for further research.
Reinforcement Learning; Data Analysis; Machine Learning; Decision-Making; Predictive Analytics; AI-Driven Approaches; Traditional Data Methods
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Vinayak Pillai. Enhancing data analyst decision-making with reinforcement learning: A comparative study of traditional vs AI-driven approaches. World Journal of Advanced Research and Reviews, 2024, 23(2), 1958-1975. Article DOI: https://doi.org/10.30574/wjarr.2024.23.2.2540