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_aSpringerLink (Online service) _9106996 |
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_c111010 _d111010 |
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| 003 | ES-MaUEC | ||
| 005 | 20230102113449.0 | ||
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| 007 | cr nn nnnaamaa | ||
| 008 | 190312s2019 gw a o |||| 0|eng d | ||
| 020 | _a9783030140380 | ||
| 024 | 7 |
_a10.1007/978-3-030-14038-0 _2doi |
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| 040 |
_bspa _dES-MaUEC _cES-MaUEC |
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| 050 | 4 |
_a QA76.9.D343 _b2019 EB |
|
| 100 | 1 |
_aMcCarthy, Richard V. _eautor _4aut _9673177 |
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| 245 | 1 | 0 |
_aApplying Predictive Analytics : _bFinding Value in Data _cby Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi. |
| 264 | 1 |
_aCham _bSpringer International Publishing : _bImprint: Springer _c2019. |
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| 300 | _a1 recurso en línea (X, 205 páginas) | ||
| 336 |
_2rdacontent _aTexto _btxt |
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| 337 |
_2rdamedia _aelectrónico _bc |
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| 338 |
_2rdacarrier _arecurso electrónico _bcr |
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| 347 |
_atext file _bPDF |
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| 490 | 0 | _aEngineering (Springer-11647) | |
| 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 | 3 | _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. | |
| 988 | _aPrimersemestre_2019_Engineering | ||
| 650 | 7 |
_2embne _aData mining _9162648 |
|
| 700 | 1 |
_aCeccucci, Wendy. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 700 | 1 |
_aHalawi, Leila. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 700 | 1 |
_aMcCarthy, Mary M. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 776 | 0 | 8 |
_iPrinted edition: _z9783030140373 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030140397 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030140403 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-14038-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
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_dz _feng _ggw _h0 _b04/2020 _ea _zSI |
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