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Informative Article | Computer Science and Information Technology | India | Volume 9 Issue 8, August 2020 | Popularity: 5.5 / 10

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Machine Learning Algorithms for Predictive Analytics in E-Commerce

Tarun Gupta, Supriya Bansal

Abstract: This article focuses on the fast - changing and problematic domain of the collision of machine learning algorithms and predictive analytics in the e - commerce sector, with the ultimate goal of presenting their many - faceted applications, their strengths, challenges, and future approaches. The research has traversed through the different clusters of machine learning, looking into the suitability of supervised and unsupervised learning, ensemble methods, and deep learning in tackling the intricacies of e - commerce challenges such as demand forecasting, customer segmentation, and personalized marketing. Additionally, the research directs its attention to the combination of cognitive computing and emotional intelligence, explores their compatibility with machine learning, and thus provides a textured comprehension of human conduct. With breakthroughs in the field, critical issues such as data quality, interpretability, and ethical concerns remain. Thus, the quest for more exploration is ongoing. The findings highlight beneficial pursuits for future research, concentrating on enhancing the interpretability of algorithms, incorporating future technologies, adapting to variable market conditions, and considering the ethical dimensions. This paper aims to merge existing knowledge and chart future research directions to contribute a holistic understanding that will empower business entities to exploit every bit of the possibilities presented by present and future machine learning algorithms for predictive analytics in the existing e - commerce industry.

Keywords: Machine learning algorithms, Predictive analytics, E - commerce, Cognitive computing, Emotional intelligence

Edition: Volume 9 Issue 8, August 2020

Pages: 1550 - 1557

DOI: https://www.doi.org/10.21275/SR24302212009

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