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Titlebook: Applying Predictive Analytics; Finding Value in Dat Richard V. McCarthy,Mary M. McCarthy,Leila Halawi Textbook 20191st edition Springer Nat

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期刊全称Applying Predictive Analytics
期刊简称Finding Value in Dat
影响因子2023Richard V. McCarthy,Mary M. McCarthy,Leila Halawi
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发行地址Focuses on how to use predictive analytic techniques to analyze historical data for the purpose of predicting future results.Takes an applied approach and focus on solving business problems using pred
图书封面Titlebook: Applying Predictive Analytics; Finding Value in Dat Richard V. McCarthy,Mary M. McCarthy,Leila Halawi Textbook 20191st edition Springer Nat
影响因子.This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life example of how business analytics have been used in various aspects of organizations to solve issue or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes..
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Identifying the Determinants of Justice analytics consists primary of the “Big 3” techniques: regression analysis, decision trees, and neural networks. Although several other techniques, such as random forests and ensemble models, have become increasingly popular in their use, predictive analytics focuses on building and evaluating predi
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The Experiences of Pupils Educated Otherwiseand prepare the data for predictive modeling. Most raw data is considered “dirty” or “noisy” because the data may have incomplete information, redundant information, outliers, or errors. Therefore, the data should be analyzed and “cleaned” prior to model development. Chap. . outlines the entire data
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https://doi.org/10.1057/9780230277335ive model. Popular regression models include linear regression, logistic regression, principal component regression, and partial least squares. This chapter defines these techniques and when it is appropriate to use the various regression models. Regression assumptions for each type are discussed. E
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