BERT-CNN-A-BiGRU Approach for Predictive Analytics in E-commerce

This research study investigates the potential impact of predictive analytics on consumer privacy within the e-commerce domain. While predictive analytics offers significant benefits to businesses, it raises concerns regarding the privacy of personal information. The study explores the techniques involved in data preprocessing, feature extraction, and model training using machine learning algorithms. A key focus is on the integration of K-Means clustering and a unified BERT-CNN-A-BiGRU model to enhance prediction accuracy. The proposed approach outperforms state-of-the-art methods, achieving an accuracy level of 98.30%. This improvement highlights the effectiveness of the proposed methodology in addressing the privacy concerns associated with predictive analytics in e-commerce. By understanding the potential risks and benefits of predictive analytics, it is crucial to strike a balance between personalized experiences and consumer privacy. This research contributes to the ongoing dialogue on ethical data practices and responsible use of AI in E-commerce.