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Informationen zum Autor Dr. Neha Singh is an Assistant Professor in the Electronics & Communication Engineering Department at Manipal University Jaipur, India. Dr. Shilpi Birla is an Associate Professor in the Electronics & Communication Department at Manipal University Jaipur, India. Dr. Mohd Dilshad Ansari is an Associate Professor in the Computer Science & Engineering Department at SRM University Delhi-NCR, Sonepat, Haryana, India. Dr. Neeraj Kumar Shukla is an Associate Professor in the Electrical Engineering Department at King Khalid University, Saudi Arabia. Klappentext 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. Inhaltsverzeichnis About the Editors xiii List of Contributors xv Preface xix Acknowledgments xxi 1 Data Mining for Predictive Analytics 1 Prakash Kuppuswamy, Mohd Dilshad Ansari, M. Mohan, and Sayed Q.Y. Al Khalidi 1.1 Introduction 1 1.2 Background Study 3 1.3 Applications of Data Mining 4 1.4 Challenges of Data Analytics in Data Mining 7 1.5 Significance of Data Analytics Tools for Data Mining 7 1.6 Life Cycle of Data Analytics 8 1.7 Predictive Analytics Model 11 1.8 Data Analytics Tools 14 1.9 Benefits of Predictive Analytics Techniques 18 1.10 Applications of Predictive Analytics Model 18 1.11 Conclusion 20 2 Challenges in Building Predictive Models 25 Rakesh Nayak, Ch. Rajaramesh, and Umashankar Ghugar 2.1 Introduction 25 2.2 Literature Survey 30 2.3 Few Suggestions to Overcome the Above Challenges 42 2.4 Conclusion and Future Directions 44 3 AI-driven Digital Twin and Resource Optimizat...
