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02794nmm a2200373 u 4500 |
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EB001865144 |
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EBX01000000000000001029235 |
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00000000000000.0 |
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190425 ||| eng |
| 020 |
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|a 9783030140380
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| 100 |
1 |
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|a McCarthy, Richard V.
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| 245 |
0 |
0 |
|a Applying Predictive Analytics
|h Elektronische Ressource
|b Finding Value in Data
|c by Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi
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| 250 |
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|a 1st ed. 2019
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2019, 2019
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| 300 |
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|a X, 205 p
|b online resource
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| 505 |
0 |
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|a Introduction to Predictive Analytics -- Know Your Data – Data Preparation -- What do Descriptive Statistics Tell Us -- The First of the Big Three – Regression -- The Second of the Big Three – Decision Trees -- The Third of the Big Three - Neural Networks -- Model Comparisons and Scoring -- Appendix A -- Data Dictionary for the Automobile Insurance Claim Fraud Data Example -- Conclusion
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| 653 |
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|a Data Analysis and Big Data
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Quantitative research
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| 653 |
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|a Data mining
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Telecommunication
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| 653 |
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|a Communications Engineering, Networks
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| 653 |
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|a Data Mining and Knowledge Discovery
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| 700 |
1 |
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|a McCarthy, Mary M.
|e [author]
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| 700 |
1 |
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|a Ceccucci, Wendy
|e [author]
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| 700 |
1 |
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|a Halawi, Leila
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
5 |
0 |
|a 10.1007/978-3-030-14038-0
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-030-14038-0?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 621.382
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| 520 |
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|a 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. 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 predictive analytics and featurescase studies and a variety of examples; Uses examples in SAS Enterprise Miner, one of world’s leading analytics software tools
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