data mining and predictive analytics wiley pdf

Data Mining And Predictive Analytics Wiley Pdf

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The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.

Discovering knowledge in Data: an Introduction Data Mining. Ed. 2

Predictive analytics encompasses a variety of statistical techniques from data mining , predictive modelling , and machine learning , that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score probability for each individual customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. One of the best-known applications is credit scoring , [1] which is used throughout business management.

Data Mining and Predictive Analytics (Wiley Series on Methods and Applications)

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Larose and C. Larose , C. Larose Published Computer Science. Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified white box approach to data mining methods and models.


This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic.


DATA MINING FOR BUSINESS ANALYTICS

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Our editors have compiled this directory of the best predictive analytics books based on Amazon user reviews, rating, and ability to add business value. There are few resources that can match the in-depth, comprehensive detail of one of the best data predictive analytics books. The editors at Solutions Review have done much of the work for you, curating this directory of the best predictive analytics books on Amazon. Titles have been selected based on the total number and quality of reader user reviews and ability to add business value. Each of the books listed in this compilation meets a minimum criteria of 10 reviews and a 4-star-or-better ranking.

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The 17 Best Predictive Analytics Books on Our Reading List

Modern science and engineering are based on using first — principle models to describe physical, biological, and social systems. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Data Mining uses raw data to extract information or in fact, mining the required information from data.

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