A Study On Ethical Challenges And Bias In Machine Learning Algorithms
Keywords:
Machine learning, Consumer behaviour analysis, Data optimization, Feature engineering, Ethical considerationsAbstract
In the realm of consumer behaviour analysis, the confluence of machine learning methodologies and abundant data resources has propelled the discipline to new heights. This comprehensive review delves into the latest frontiers of deploying machine learning algorithms to decode the intricacies of consumer behaviour. The investigation initiates with an in-depth exploration of data acquisition and optimization, elucidating the meticulous process of feature engineering, and scrutinizing the practical application of diverse machine learning paradigms, encompassing regression, classification, clustering, and recommendation systems. Furthermore, the discourse encompasses the tangible ramifications of machine learning derived insights, elucidating their utility in optimizing pricing strategies, guiding product development, orchestrating targeted marketing campaigns, and enhancing the Caliber of customer service. It is essential to underscore the complexity of the challenges and ethical considerations intrinsic to the computational analysis of consumer behaviour.
In conclusion, this review casts a forward-looking perspective, highlighting the imperative need for an ever-evolving data landscape to adapt to the ever-shifting consumer preferences and market dynamics. This paper serves as a paramount reference for researchers, businesses, and policymakers aiming to harness the multifaceted potential of machine learning in the realm of consumer behaviour understanding and influence.
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