LEADER 03935nam 22006375 450 001 9910337636303321 005 20251116212044.0 010 $a3-030-14038-5 024 7 $a10.1007/978-3-030-14038-0 035 $a(CKB)4100000007810210 035 $a(DE-He213)978-3-030-14038-0 035 $a(MiAaPQ)EBC5925363 035 $a(PPN)235234745 035 $a(EXLCZ)994100000007810210 100 $a20190312d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplying Predictive Analytics $eFinding Value in Data /$fby Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (X, 205 p.) 311 08$a3-030-14037-7 327 $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. 330 $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. 606 $aElectrical engineering 606 $aComputational intelligence 606 $aData mining 606 $aBig data 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 615 0$aElectrical engineering. 615 0$aComputational intelligence. 615 0$aData mining. 615 0$aBig data. 615 14$aCommunications Engineering, Networks. 615 24$aComputational Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aBig Data/Analytics. 676 $a006.312 676 $a006.312 700 $aMcCarthy$b Richard V.$4aut$4http://id.loc.gov/vocabulary/relators/aut$098286 702 $aMcCarthy$b Mary M.$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCeccucci$b Wendy$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aHalawi$b Leila$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910337636303321 996 $aApplying Predictive Analytics$92591239 997 $aUNINA