| 000 | 03735nam a22005775i 4500 | ||
|---|---|---|---|
| 001 | 978-3-030-14038-0 | ||
| 003 | DE-He213 | ||
| 005 | 20220801214833.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 190312s2019 sz | s |||| 0|eng d | ||
| 020 |
_a9783030140380 _9978-3-030-14038-0 |
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| 024 | 7 |
_a10.1007/978-3-030-14038-0 _2doi |
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_a621.382 _223 |
| 100 | 1 |
_aMcCarthy, Richard V. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _940765 |
|
| 245 | 1 | 0 |
_aApplying Predictive Analytics _h[electronic resource] : _bFinding Value in Data / _cby Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi. |
| 250 | _a1st ed. 2019. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
|
| 300 |
_aX, 205 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _aIntroduction 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. | |
| 520 | _aThis 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 features case studies and a variety of examples; Uses examples in SAS Enterprise Miner, one of world’s leading analytics software tools. | ||
| 650 | 0 |
_aTelecommunication. _910437 |
|
| 650 | 0 |
_aComputational intelligence. _97716 |
|
| 650 | 0 |
_aData mining. _93907 |
|
| 650 | 0 |
_aQuantitative research. _94633 |
|
| 650 | 1 | 4 |
_aCommunications Engineering, Networks. _931570 |
| 650 | 2 | 4 |
_aComputational Intelligence. _97716 |
| 650 | 2 | 4 |
_aData Mining and Knowledge Discovery. _940766 |
| 650 | 2 | 4 |
_aData Analysis and Big Data. _940767 |
| 700 | 1 |
_aMcCarthy, Mary M. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _940768 |
|
| 700 | 1 |
_aCeccucci, Wendy. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _940769 |
|
| 700 | 1 |
_aHalawi, Leila. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _940770 |
|
| 710 | 2 |
_aSpringerLink (Online service) _940771 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030140373 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030140397 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030140403 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-14038-0 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c76807 _d76807 |
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