Predictive analytics is the art and science of proposed predictive systems and models. With tuning over time, these models can predict an outcome with a far higher statistical probability than mere guesswork. Predictive analytics plays an essential role in the digital era. Most of the business strategies and planning depend on prediction and analytics using statistical approaches.
With the increasing digitization day by day, analytical challenges are also increasing at the same rate, such as digital information, which is rapidly growing, generating vast amounts of data. There are numerous statistical approaches to perform predictive analytics, including Bayesian analysis, Sequential analysis, Statistical prediction, risk prediction, and decision analytics.
Predictive Analytics Using Statistics and Big Data: Concepts and Modeling presents some latest and representative developments in predictive analytics using big data technologies. It focuses on some critical aspects of big data and machine learning and provides descriptions for these technologies. The proposed book addresses a comprehensive range of advanced data technologies with statistical modeling towards predictive analytics. This book will be of significant benefit to the community as a useful guide of the latest research in this emerging field, i.e., predictive analytics.
About the Editor(s):
Dr. Krishna Kumar Mohbey is an Assistant Professor of Computer Science at the Central University of Rajasthan, India. He received his bachelor's degree in Computer Application from MCRPV Bhopal (2006), Master's in Computer Application from Rajiv Gandhi Technological University Bhopal (2009), and Ph.D. from Department of Mathematics and Computer Applications from National Institute of Technology Bhopal, India (2015). His areas of interest are data mining, mobile web services, big data analysis, and user behavior predictions.
Dr. Arvind Pandey is an Associate Professor in the Statistics Department at the Central University of Rajasthan, India. He received his Ph.D. from the Department of Statistics, Savitribai Phule Pune University, Maharastra, India, specializing in frailty models. His current interests include survival analysis, Statistical inference, and Bayesian Analysis and Distribution theory. He has published more than 30 research papers in leading journals and conferences in statistics and data sciences.
Dr. Dharmendra Singh Rajput is working as an Associate Professor in the Department of Software and Systems Engineering, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. He was a Full-Time research scholar in MANIT Bhopal from January 2011 to June 2014. He has completed his Ph.D. in the area of Data Mining. The topic of his research is "Frequent Termset based Models for Document Clustering." He has published good research papers in journals of International repute. He had already presented a good number of research papers at National/ International level conferences. He had visited various countries like- U.K., Malaysia, France, Singapore, and UAE for academic purposes. Now his research areas are Machine Learning and Big Data Predictive Analytics.
Predictive analytics, Statistical prediction, big data technologies, digitization, digital information,
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