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Harshad M. Kubade is affiliated with the Department of Information Technology at Priyadarshini College of Engineering, Nagpur, Maharashtra, India. His academic affiliation is associated with information technology. His contribution addresses machine learning applications for consumer analytics, e-commerce, and intelligent recommendation systems.
The swift expansion of internet commerce has revolutionised the manner in which companies engage with customers, oversee product stocks, tailor shopping experiences, and formulate strategic choices. The growing accessibility of customer profiles, browsing histories, buy records, product evaluations, search enquiries, and clickstream information has generated prospects for utilising machine learning (ML) to comprehend consumer behaviour and enhance corporate efficacy. This chapter explores the essential principles, algorithms, frameworks, applications, and assessment techniques related to machine learning-based e-commerce and consumer analytics. It examines supervised learning, unsupervised learning, deep learning, natural language processing, collaborative filtering, content-based filtering, hybrid recommendation, and sequential recommendation. A theoretical structure is proposed for the amalgamation of data collection, preprocessing, consumer modelling, suggestion formulation, assessment, and ongoing feedback. Mathematical models for forecasting customer behaviour, rating recommendations, and assessing models are presented.