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Commissioners are grasping the potential of data analytics to predict demand for children's services, says Richard Selwyn

Business today relies on effectively using data to predict trends and sales. Predictive analytics is the tool that can make it happen, and this book eliminates the tricks and shows you how to use it. You'll learn to prepare and process your data, create goals, build a predictive model, get your organization's stakeholders on board and more

This book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques. It will also be useful to students learning reliability engineering. The book brings readers up to date with how system reliability relates to the latest techniques of AI, big data, genetic algorithm, information theory, and multi-criteria decision making and points toward future developments in the subject.

Create and run a human resource analytics project with confidence For any human resource professional that wants to harness the power of analytics, this essential resource answers the questions: "Where do I start?" and "What tools are available?" Predictive Analytics for Human Resources is designed to answer these and other vital questions. The book explains the basics of every business-the vision, the brand, and the culture, and shows how predictive analytics supports them. The authors put the focus on the fundamentals of predictability and include a framework of logical questions to help set up an analytic program or project, then follow up by offering a clear explanation of statistical applications. Predictive Analytics for Human Resources is a how-to guide filled with practical and targeted advice. The book starts with the basic idea of engaging in predictive analytics and walks through case simulations showing statistical examples. In addition, this important resource addresses the topics of internal coaching, mentoring, and sponsoring and includes information on how to recruit a sponsor. In the book, you'll find:A comprehensive guide to developing and implementing a human resource analytics projectIllustrative examples that show how to go to market, develop a leadership model, and link it to financial targets through causal modelingExplanations of the ten steps required in building an analytics functionHow to add value through analysis of systems such as staffing, training, and retention For anyone who wants to launch an analytics project or program for HR, this complete guide provides the information and instruction to get started the right way.

Chapter 1 Role of MCDM in Software Reliability Engineering Chapter 2 Fault Tree Analysis of a Computerized Numerical Control Turning Center Chapter 3 How to Schedule Elective Patients in Hospitals to Gain Full Utilization of Resources and Eliminate Patient Overcrowding Chapter 4 Reducing the Deterioration Rate of Inventory through Preservation Technology Investment under Fuzzy and Cloud Fuzzy Environment Chapter 5 Image Formation Using Deep Convolutional Generative Adversarial Networks Chapter 6 Optimal Preservation Technology Investment and Price for the Deteriorating Inventory Model with Price-Sensitivity Stock- Dependent Demand Chapter 7 EOQ with Shortages and Learning Effect Chapter 8 Optimal Production-Inventory Policies for Processed Fruit Juices Manufacturer and Multi-retailers with Trended Demand and Quality Degradation Chapter 9 Information Visualization: Perception and Limitations for Data-Driven Designs Chapter 10 IoT, Big Data, and Analytics -- Challenges and Opportunities Chapter 11 Multiple-Criteria Decision Analysis Using VLSI Global Routing Chapter 12 Application of IoT in Water Supply Management Chapter 13 A Hybrid Approach for Video Indexing Using Computer Vision and Speech Recognition Chapter 14 Statistical Methodology for Software Reliability with Environmental Factors Chapter 15 Maintenance Data-Trends Based Reliability Availability and Maintainability (RAM) Assessment of a Steam Boiler

A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. Key Features: Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Accompanied by a website featuring material from each case study, including datasets and relevant code. Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.

"A high-level, informal look at the different stages of the predictive analytics cycleUnderstanding the Predictive Analytics Lifecycle covers each phase of the development of a predictive analytics initiative. Through the use of illuminating case studies across a range of industries that include banking, megaresorts, mobile operators, healthcare, manufacturing, and retail, the book successfully illustrates each phase of the predictive analytics cycle to create a playbook for future projects.Predictive business analytics involves a wide variety of inputs that include individuals' skills, technologies, tools, and processes. To create a successful analytics program or project to gain forward-looking insight into making business decisions and actions, all of these factors must properly align. The book focuses on developing new insights and understanding business performance based on extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management as input for human decisions. The book includes: An overview of all relevant phases: design, prepare, explore, model, communicate, and measure Coverage of the stages of the predictive analytics cycle across different industries and countries A chapter dedicated to each of the phases of the development of a predictive initiative A comprehensive overview of the entire analytic process lifecycle If you're an executive looking to understand the predictive analytics lifecycle, this is a must-read resource and reference guide"--