LEADER 05331nam 2200661 450 001 9910812118003321 005 20200520144314.0 010 $a1-118-72769-X 010 $a1-118-72793-2 035 $a(CKB)2550000001256699 035 $a(EBL)1662190 035 $a(SSID)ssj0001211417 035 $a(PQKBManifestationID)11782320 035 $a(PQKBTitleCode)TC0001211417 035 $a(PQKBWorkID)11205278 035 $a(PQKB)10247432 035 $a(MiAaPQ)EBC1662190 035 $a(Au-PeEL)EBL1662190 035 $a(CaPaEBR)ebr10856845 035 $a(CaONFJC)MIL588422 035 $a(OCoLC)878149193 035 $a(PPN)186134347 035 $a(EXLCZ)992550000001256699 100 $a20140414h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplied predictive analytics $eprinciples and techniques for the professional data analyst /$fDean Abbott 210 1$aIndianapolis, Indiana :$cJohn Wiley & Sons,$d2014. 210 4$dİ2014 215 $a1 online resource (453 p.) 300 $aIncludes index. 311 $a1-118-72796-7 311 $a1-306-57171-5 327 $aCover; Title Page; Copyright; Contents; Chapter 1 Overview of Predictive Analytics; What Is Analytics?; What Is Predictive Analytics?; Supervised vs. Unsupervised Learning; Parametric vs. Non-Parametric Models; Business Intelligence; Predictive Analytics vs. Business Intelligence; Do Predictive Models Just State the Obvious?; Similarities between Business Intelligence and Predictive Analytics; Predictive Analytics vs. Statistics; Statistics and Analytics; Predictive Analytics and Statistics Contrasted; Predictive Analytics vs. Data Mining; Who Uses Predictive Analytics? 327 $aChallenges in Using Predictive AnalyticsObstacles in Management; Obstacles with Data; Obstacles with Modeling; Obstacles in Deployment; What Educational Background Is Needed to Become a Predictive Modeler?; Chapter 2 Setting Up the Problem; Predictive Analytics Processing Steps: CRISP-DM; Business Understanding; The Three-Legged Stool; Business Objectives; Defining Data for Predictive Modeling; Defining the Columns as Measures; Defining the Unit of Analysis; Which Unit of Analysis?; Defining the Target Variable; Temporal Considerations for Target Variable 327 $aDefining Measures of Success for Predictive ModelsSuccess Criteria for Classification; Success Criteria for Estimation; Other Customized Success Criteria; Doing Predictive Modeling Out of Order; Building Models First; Early Model Deployment; Case Study: Recovering Lapsed Donors; Overview; Business Objectives; Data for the Competition; The Target Variables; Modeling Objectives; Model Selection and Evaluation Criteria; Model Deployment; Case Study: Fraud Detection; Overview; Business Objectives; Data for the Project; The Target Variables; Modeling Objectives 327 $aModel Selection and Evaluation CriteriaModel Deployment; Summary; Chapter 3 Data Understanding; What the Data Looks Like; Single Variable Summaries; Mean; Standard Deviation; The Normal Distribution; Uniform Distribution; Applying Simple Statistics in Data Understanding; Skewness; Kurtosis; Rank-Ordered Statistics; Categorical Variable Assessment; Data Visualization in One Dimension; Histograms; Multiple Variable Summaries; Hidden Value in Variable Interactions: Simpson's Paradox; The Combinatorial Explosion of Interactions; Correlations; Spurious Correlations; Back to Correlations; Crosstabs 327 $aData Visualization, Two or Higher DimensionsScatterplots; Anscombe's Quartet; Scatterplot Matrices; Overlaying the Target Variable in Summary; Scatterplots in More Than Two Dimensions; The Value of Statistical Significance; Pulling It All Together into a Data Audit; Summary; Chapter 4 Data Preparation; Variable Cleaning; Incorrect Values; Consistency in Data Formats; Outliers; Multidimensional Outliers; Missing Values; Fixing Missing Data; Feature Creation; Simple Variable Transformations; Fixing Skew; Binning Continuous Variables; Numeric Variable Scaling; Nominal Variable Transformation 327 $aOrdinal Variable Transformations 330 $a Learn the art and science of predictive analytics - techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful p 606 $aBusiness$xData processing 606 $aBusiness planning$xData processing 606 $aBusiness$xComputer programs 615 0$aBusiness$xData processing. 615 0$aBusiness planning$xData processing. 615 0$aBusiness$xComputer programs. 676 $a006.312 700 $aAbbott$b Dean$01719101 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910812118003321 996 $aApplied predictive analytics$94116619 997 $aUNINA