Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

In an accessible yet fastidiously researched intervention Early Detection sets out the urgent necessity of fundamentally re-directing the US’s approach to cancer treatment if President Biden’s recently announced prioritization of the issue is to be successful. Catching cancer early remains the single best approach to fighting this deadly disease, the second-leading killer both in the US and worldwide. Yet, the health system often fails to do so, even when the necessary tools are available. Early Detection looks at shortcomings in cancer screening efforts and how  early detection procedures can be expanded and improved.. Early Detection explores cancer screening systematically and scientifically, examining the subject from the level of individual tests all the way up to the roles and incentives of large healthcare systems and the federal government. It looks not only at the scientific challenges involved but also the social and organizational challenges, an angle that has been traditionally under-covered but is especially relevant in light of the COVID-19 pandemic. The book also highlights the disparities of race and economic class that affect access to early screening. This problem exists throughout the medical system overall but, when it comes to early detection, the problem becomes especially far-reaching from both an ethical and an economic point of view. In teaming together, Bruce Ratner and Adam Bonislawski combine the passions of someone touched deeply by the experience of cancer and the cool analysis of an expert in medical policy and science. They tackle the subject with a combination of breadth and granularity, exploring why early detection has not been given the level of priority it deserves, and the ways it can dramatically reduce cancer deaths in this country.