Applied predictive analytics : principles and techniques for the professional data analyst

Dean Abbott

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 predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

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Year of publication:	[2014]
Authors:	Abbott, Dean
Publisher:	Indianapolis, Ind. : Wiley
Subject:	Unternehmensplanung \| Corporate planning \| Management-Informationssystem \| Management information system \| Data Mining \| Data mining \| Mathematik \| Mathematics \| Managementinformationssystem \| Datenanalyse \| Prognose
Description of contents:	Description [swbplus.bsz-bw.de]

Extent:	1 Online-Resssourc (XXIV, 427 Seiten) Diagramme
Series:	ProQuest Ebook Central
Type of publication:	Book / Working Paper
Language:	English
Notes:	Description based on publisher supplied metadata and other sources. Cover; 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? Challenges 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 Defining 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 Model 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 Data 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 Ordinal Variable Transformations Chapter 1: Overview of Predictive Analytics; What Is Analytics?; What Is Predictive Analytics?; Business Intelligence; Predictive Analytics vs. Business Intelligence; Predictive Analytics vs. Statistics; Predictive Analytics vs. Data Mining; Who Uses Predictive Analytics?; Challenges in Using Predictive Analytics; What Educational Background Is Needed to Become a Predictive Modeler?; Chapter 2: Setting Up the Problem; Predictive Analytics Processing Steps: CRISP-DM; Business Understanding; Defining Data for Predictive Modeling; Defining the Target Variable Defining Measures of Success for Predictive ModelsDoing Predictive Modeling Out of Order; Case Study: Recovering Lapsed Donors; Case Study: Fraud Detection; Summary; Chapter 3: Data Understanding; What the Data Looks Like; Single Variable Summaries; Data Visualization in One Dimension; Histograms; Multiple Variable Summaries; The Value of Statistical Significance; Pulling It All Together into a Data Audit; Summary; Chapter 4: Data Preparation; Variable Cleaning; Feature Creation; Summary; Chapter 5: Itemsets and Association Rules; Terminology; Parameter Settings; How the Data Is Organized Deploying Association RulesProblems with Association Rules; Building Classification Rules from Association Rules; Summary; Chapter 6: Descriptive Modeling; Data Preparation Issues with Descriptive Modeling; Principal Component Analysis; Clustering Algorithms; Summary; Chapter 7: Interpreting Descriptive Models; Standard Cluster Model Interpretation; Summary; Chapter 8: Predictive Modeling; Decision Trees; Logistic Regression; Neural Networks; K-Nearest Neighbor; Naïve Bayes; Regression Models; Linear Regression; Other Regression Algorithms; Summary; Chapter 9: Assessing Predictive Models Batch Approach to Model AssessmentAssessing Regression Models; Summary; Chapter 10: Model Ensembles; Motivation for Ensembles; Bagging; Boosting; Improvements to Bagging and Boosting; Model Ensembles and Occam's Razor; Interpreting Model Ensembles; Summary; Chapter 11: Text Mining; Motivation for Text Mining; A Predictive Modeling Approach to Text Mining; Structured vs. Unstructured Data; Why Text Mining Is Hard; Data Preparation Steps; Text Mining Features; Modeling with Text Mining Features; Regular Expressions; Summary; Chapter 12: Model Deployment; General Deployment Considerations SummaryChapter 13: Case Studies; Survey Analysis Case Study: Overview; Help Desk Case Study; Introduction; How This Book Is Organized; Who Should Read This Book; Tools You Will Need; What's on the Website; Summary; End User License Agreement
ISBN:	978-1-118-72793-5 ; 1-306-57171-5 ; 978-1-306-57171-5 ; 978-1-118-72796-6
Classification:	Methoden und Techniken der Betriebswirtschaft ; Künstliche Intelligenz
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014461188