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Comprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries
Intelligent Techniques for Predictive Data Analytics provides an in-depth introduction of the tools and techniques used for predictive analytics, covering applications in cyber security, network security, data mining, and machine learning across various industries. Each chapter offers a brief introduction on the subject to make the text accessible regardless of background knowledge.
Readers will gain a clear understanding of how to use data processing, classification, and analysis to support strategic decisions, such as optimizing marketing strategies and customer relationship management and recommendation systems, improving general business operations, and predicting occurrence of chronic diseases for better patient management.
Traditional data analytics uses dashboards to illustrate trends and outliers, but with large data sets, this process is labor-intensive and time-consuming. This book provides everything readers need to save time by performing deep, efficient analysis without human bias and time constraints. A section on current challenges in the field is also included.
Intelligent Techniques for Predictive Data Analytics covers sample topics such as:
Models to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models Price forecasting, quality optimization, and insect and disease plant and monitoring in agriculture Fraud detection and prevention, credit scoring, financial planning, and customer analytics Big data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting Management of uncertainty in predictive data analytics and probable future developments in the field
Intelligent Techniques for Predictive Data Analytics is an essential resource on the subject for professionals and researchers working in data science or data management seeking to understand the different models of predictive analytics, along with graduate students studying data science courses and professionals and academics new to the field.
About the Editors
List of Contributors
Preface
Acknowledgments
Data Mining for Predictive Analytics
Challenges in Building Predictive Models
AI-driven Digital Twin and Resource Optimization in Industry 4.0 Ecosystem
Predictive Analytics in Healthcare
A Review of Automated Sleep Stage Scoring Using Machine Learning Techniques Based on Physiological Signals
Predictive Analytics for Marketing and Sales of Products Using Smart Trolley with Automated Billing System in Shopping Malls Using LBPH and Faster R-CNN
Enhancing Stock Market Predictions Through Predictive Analytics
Predictive Analytics and Cybersecurity
Precision Agriculture and Predictive Analytics: Enhancing Agricultural Efficiency and Yield
A Simple Way to Comprehend the Difference and the Significance of Artificial Intelligence in Agriculture
An Overview of Predictive Maintenance and Load Forecasting
Predictive Analytics: A Tool for Strategic Decision of Employee Turnover
Index