Online shopping behavior analysis for smart business using big data analytics and blockchain security

Abstract

Technological advancement in a modern environment is approaching universality. The realm and uses of information technology (IT) and Blockchain Security have been extensively broadened. Any business wishing to boost its market prospects would certainly keep records of the perspectives and behaviors of its consumers. The firms employ advanced technological concepts, skills, and methods to comprehend their applicants. Additional information and facts are analyzed to improve judgment. Business analytics and Blockchain Security professionals have a positive opinion. Online shopping behavior analysis (OSBA) is proposed in this research. It addresses a smooth transition from a prediction method to a gradual information strategy that learns the clients’ needs and achieves their electronic trading revenues. Any commercial enterprise must have limitless entry to information. That contains population revenues, industrial patterns, competition and customer information, productivity measurements, computations, and much more. Corporate information has a significant role in this undertaking. Experimental information is collected periodically to evaluate the evidence and provide fresh discoveries and operations that provide fresh perspectives. The major consideration principal element assessment approach is utilized with big data analytics, Blockchain Security, and fuzzy interference system to assess the essential purchasing variables for customers. It achieves an accuracy of 89% and an [Formula: see text] score of 87%. Models like support vector machine, convolutional neural network, deep neural network, random forest, fuzzy logic, and decision tree (DT) are compared with the OSBA model’s simulation results (DT). Fuzzy interference, big data, and Blockchain Security analytics improve the OSBA model’s performance.