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Nearly all our safety data collection and reporting systems are backwardlooking: incident reports; dashboards; compliance monitoring systems; and so on. This book shows how we can use safety data in a forward-looking, predictive sense. Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where organizations have reduced incidents by employing predictive analytics to foresee and mitigate future risks. It discusses how Predictive Safety Analytics is an opportunity to break through the plateau problem where safety rate improvements have stagnated in many organizations. The book presents how the use of data, coupled with advanced analytical techniques, including machine learning, has become a proven and successful innovation. Emphasis is placed on how the book can “meet you where you are” by illuminating a path to get there, starting with simple data the organization likely already has. Highlights of the book are the real examples and case studies that will assist in generating thoughts and ideas for what might work for individual readers and how they can adapt the information to their particular situations. This book is written for professionals and researchers in system reliability, risk and safety assessment, quality control, operational managers in selected industries, data scientists, and ML engineers. Students taking courses in these areas will also find this book of interest to them.
"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.
Highway Safety Analytics and Modeling by Dominique Lord,Xiao Qin,Srinivas R. Geedipally Pdf
Highway Safety Analytics and Modeling comprehensively covers the key elements needed to make effective transportation engineering and policy decisions based on highway safety data analysis in a single. reference. The book includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating analysis results. It discusses the challenges of working with crash and naturalistic data, identifies problems and proposes well-researched methods to solve them. Finally, the book examines the nuances associated with safety data analysis and shows how to best use the information to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes. Complements the Highway Safety Manual by the American Association of State Highway and Transportation Officials Provides examples and case studies for most models and methods Includes learning aids such as online data, examples and solutions to problems
Impact of AI on Advancing Women's Safety by Ponnusamy, Sivaram,Bora, Vibha,Daigavane, Prema M.,Wazalwar, Sampada S. Pdf
Women encounter multifaceted threats, ranging from personal safety hazards to discrimination deeply embedded in societal structures. The existing landscape demands innovative strategies to ensure women can participate fully in society without fear or impediment. Traditional systems often fall short, necessitating a paradigm shift in our approach to women's safety. Impact of AI on Advancing Women's Safety emerges as a groundbreaking solution to address the pervasive challenges they face. From the shadows of harassment to systemic biases in justice systems, women navigate a complex landscape. This book delves into the pressing issues, unveiling a visionary approach that leverages artificial intelligence to create tangible, transformative solutions.
AI Tools and Applications for Women’s Safety by Ponnusamy, Sivaram,Bora, Vibha,Daigavane, Prema M.,Wazalwar, Sampada S. Pdf
In an era marked by rapid technological progress, women's safety remains a pressing concern despite strides toward gender equality. Women continue to grapple with safety challenges in both public and private spaces, enduring harassment, violence, and discrimination driven by entrenched societal norms and modern complexities. Amidst these challenges, harnessing the potential of artificial intelligence (AI) emerges as a promising avenue to reshape the landscape of women's safety. The groundbreaking book, AI Tools and Applications for Women’s Safety, curated by experts Sivaram Ponnusamy, Vibha Bora, Prema Daigavane, and Sampada Wazalwar, delves into the transformative power of AI to address the daily safety concerns women face. This timely volume explores innovative AI-driven resources and applications that redefine personal security, offering tailored protection through real-time threat assessment and emergency response coordination. With comprehensive insights spanning academia, law enforcement, policymaking, and advocacy, this book covers predictive safety analytics, smart surveillance, ethical considerations, and more. AI Tools and Applications for Women’s Safety not only sheds light on the promise of AI but also paves the way for informed discourse and meaningful action, ushering in a future defined by women's empowerment and security.
Practical Predictive Analytics and Decisioning Systems for Medicine by Linda Miner,Pat Bolding,Joseph Hilbe,Mitchell Goldstein,Thomas Hill,Robert Nisbet,Nephi Walton,Gary Miner Pdf
With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost–efficient manner. Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions. Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations Demonstrates methods to help sort through data to make better observations and allow you to make better predictions
SAP Predictive Analytics by Antoine Chabert,Andreas Forster,Laurent Tessier,Pierpaolo Vezzosi Pdf
Leave your crystal ball behind and peer into the future with SAPPredictive Analytics! Master predictive models--regression, time series forecasting, clustering, and more--and learn how to get SAP Predictive Analytics up and running. Discover the essential tools, from Predictive Factory and the Automated Modeler to the Data Manager and Social Network Analysis. Get predictive analysis working for you! Highlights include: Predictive models Classification models Regression models Time-series forecasting models Predictive Factory Automated Analytics Expert Analytics Social Network Analysis (SNA) Data Manager Installation Integration
Learn the art and science of predictive analytics — techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.
Predictive Analytics For Dummies by Anasse Bari,Mohamed Chaouchi,Tommy Jung Pdf
Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions. Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more. Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.
Fundamentals of Machine Learning for Predictive Data Analytics, second edition by John D. Kelleher,Brian Mac Namee,Aoife D'Arcy Pdf
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
Predictive Business Analytics by Lawrence Maisel,Gary Cokins Pdf
Discover the breakthrough tool your company can use to make winning decisions This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting. Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling Written for senior financial professionals, as well as general and divisional senior management Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions.
Preventing Workplace Incidents in Construction by Imriyas Kamardeen Pdf
The construction industry is vital to any national economy; it is also one of the industries most susceptible to workplace incidents. The unacceptably high rates of incidents in construction have huge socio-economic consequences for the victims, their families and friends, co-workers, employers and society at large. Construction safety researchers have introduced numerous strategies, models and tools through scientific inquiries involving primary data collection and analyses. While these efforts are commendable, there is a huge potential to create new knowledge and predictive models to improve construction safety by utilising already existing data about workplace incidents. In this new book, Imriyas Kamardeen argues that more sophisticated approaches need to be deployed to enable improved analyses of incident data sets and the extraction of more valuable insights, patterns and knowledge to prevent work injuries and illnesses. The book aims to apply data mining and analytic techniques to past workplace incident data to discover patterns that facilitate the development of innovative models and strategies, thereby improving work health, safety and well-being in construction, and curtailing the high rate of incidents. It is essential reading for researchers and professionals in construction, health and safety and anyone interested in data analytics.
Sleep and Cognition by Richard R. Bootzin,John F. Kihlstrom Pdf
Despite impressive advances in understanding sleep as a biological phenomenon and in understanding the nature of mental processes in the normal waking state, our knowledge of mental life during sleep remains extremely impoverished. The common identification of cognition with consciousness leaves the impression that little or no mental activity occurs during sleep. The present volume represents the Proceedings of the Arizona Conference on Sleep and Cognition, held in Tucson January 19-22, 1989. A principal concern of the conference was the implications of recent work on implicit memory and other aspects of information-processing outside of awareness for studies of cognitive processes during sleep, and the role of the sleep laboratory as a vehicle for studying various aspects of information processing outside of awareness, in the absence of the active deployment of attention. To this end, selected investigators in the area of sleep who have an interest in cognitive processes were brought together with their counterparts in the area of cognition who have an interest in sleep. We hope that one outcome of this meeting, and publication of the Proceedings, will be a new agenda for research on sleep that will carry investigators of both camps well into the 21st century.
The Future of Oil and the Energy Industry by AD Patrickson Pdf
The 230 pages of this book will equip you with everything you did not know about petroleum and the energy industry. The book covers topics like: How the oil and gas industry works, The changing oil politics, From oil scarcity to abundance, Climate change, The new energy industry and Why oil dominates every aspect of our lives. The Future of Oil and the Energy Industry, is your energy industry bible.
EXCEL 2016 PREDICTIVE ANALYTICS FOR SERIOUS DATA CRUNCHERS! Now, you can apply cutting-edge predictive analytics techniques to help your business win–and you don’t need multimillion-dollar software to do it. All the tools you need are available in Microsoft Excel 2016, and all the knowledge and skills are right here, in this book! Microsoft Excel MVP Conrad Carlberg shows you how to use Excel predictive analytics to solve real problems in areas ranging from sales and marketing to operations. Carlberg offers unprecedented insight into building powerful, credible, and reliable forecasts, helping you gain deep insights from Excel that would be difficult to uncover with costly tools such as SAS or SPSS. Fully updated for Excel 2016, this guide contains valuable new coverage of accounting for seasonality and managing complex consumer choice scenarios. Throughout, Carlberg provides downloadable Excel 2016 workbooks you can easily adapt to your own needs, plus VBA code–much of it open-source–to streamline especially complex techniques. Step by step, you’ll build on Excel skills you already have, learning advanced techniques that can help you increase revenue, reduce costs, and improve productivity. By mastering predictive analytics, you’ll gain a powerful competitive advantage for your company and yourself. Learn the “how” and “why” of using data to make better decisions, and choose the right technique for each problem Capture live real-time data from diverse sources, including third-party websites Use logistic regression to predict behaviors such as “will buy” versus “won’t buy” Distinguish random data bounces from real, fundamental changes Forecast time series with smoothing and regression Account for trends and seasonality via Holt-Winters smoothing Prevent trends from running out of control over long time horizons Construct more accurate predictions by using Solver Manage large numbers of variables and unwieldy datasets with principal components analysis and Varimax factor rotation Apply ARIMA (Box-Jenkins) techniques to build better forecasts and clarify their meaning Handle complex consumer choice problems with advanced logistic regression Benchmark Excel results against R results
