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020 _a9783030140380
024 7 _a10.1007/978-3-030-14038-0
_2doi
040 _bspa
_dES-MaUEC
_cES-MaUEC
050 4 _a QA76.9.D343
_b2019 EB
100 1 _aMcCarthy, Richard V.
_eautor
_4aut
_9673177
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.
300 _a1 recurso en línea (X, 205 páginas)
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
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
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
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
998 _dz
_feng
_ggw
_h0
_b04/2020
_ea
_zSI