Behavioural Analytics Machine Learning Approaches for Predictive Insights, VOL 3

[Emperor2011]

Behavioural Analytics Machine Learning Approaches for Predictive Insights, VOL 3 | 9.53 MB

Title: Behavioural Analytics: Machine Learning Approaches for Predictive Insights: Volume 3
Author: Shradhanjali, Panda;Leena, Priyadarshini Singh;V., Ramasamy;S., Balamurugan;
Category: Nonfiction, Computers, Database Management, Data Processing
Language: English | 201 Pages | ISBN: 9798898813932


Description:
Applied Machine Learning for IoT and Data Analytics (Volume 3) offers a comprehensive exploration of how machine learning transforms behavioural data into actionable intelligence. In an era where data-driven strategies shape competitive advantage, this volume examines how organisations can harness predictive analytics to understand patterns, anticipate risks, and unlock hidden opportunities.The book introduces the foundational principles of behavioural analytics and advances into practical machine learning applications across diverse domains. It addresses critical integration challenges such as data quality, model reliability, privacy protection, and ethical considerations-highlighting transparency and responsible data governance as essential pillars of modern analytics frameworks.Through empirical research and real-world case studies, the volume demonstrates how predictive insights can enhance employee engagement, improve customer experiences, optimise marketing performance, and support public safety initiatives. Bridging theory with applied implementation, the book equips readers with both conceptual clarity and practical strategies for deploying machine learning-driven behavioural intelligence in dynamic organisational environments.Key Features:-Comprehensive overview of behavioural analytics and predictive modelling foundations.-Application of machine learning techniques with real-world perspectives on implementation and management.-Insights into improving employee retention, customer engagement, and operational efficiency.-Discussion of integration challenges, including data quality and governance frameworks, with a focus on ethical AI, transparency, data privacy, and responsible analytics practices

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