{"product_id":"applied-predictive-analytics-isbn-9781118727966","title":"Applied Predictive Analytics","description":"\u003cp\u003e\u003cb\u003eLearn the art and science of predictive analytics — techniques that get results\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePredictive 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eThe ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today\u003c\/li\u003e \u003cli\u003eThis guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions\u003c\/li\u003e \u003cli\u003eExplains methods, principles, and techniques for conducting predictive analytics projects from start to finish\u003c\/li\u003e \u003cli\u003eIllustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios\u003c\/li\u003e \u003cli\u003eA companion website provides all the data sets used to generate the examples as well as a free trial version of software\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Predictive Analytics\u003c\/i\u003e arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.\u003c\/p\u003e \u003cp\u003eIntroduction xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Overview of Predictive Analytics 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Analytics? 3\u003c\/p\u003e \u003cp\u003eWhat Is Predictive Analytics? 3\u003c\/p\u003e \u003cp\u003eSupervised vs. Unsupervised Learning 5\u003c\/p\u003e \u003cp\u003eParametric vs. Non-Parametric Models 6\u003c\/p\u003e \u003cp\u003eBusiness Intelligence 6\u003c\/p\u003e \u003cp\u003ePredictive Analytics vs. Business Intelligence 8\u003c\/p\u003e \u003cp\u003eDo Predictive Models Just State the Obvious? 9\u003c\/p\u003e \u003cp\u003eSimilarities between Business Intelligence and Predictive Analytics 9\u003c\/p\u003e \u003cp\u003ePredictive Analytics vs. Statistics 10\u003c\/p\u003e \u003cp\u003eStatistics and Analytics 11\u003c\/p\u003e \u003cp\u003ePredictive Analytics and Statistics Contrasted 12\u003c\/p\u003e \u003cp\u003ePredictive Analytics vs. Data Mining 13\u003c\/p\u003e \u003cp\u003eWho Uses Predictive Analytics? 13\u003c\/p\u003e \u003cp\u003eChallenges in Using Predictive Analytics 14\u003c\/p\u003e \u003cp\u003eObstacles in Management 14\u003c\/p\u003e \u003cp\u003eObstacles with Data 14\u003c\/p\u003e \u003cp\u003eObstacles with Modeling 15\u003c\/p\u003e \u003cp\u003eObstacles in Deployment 16\u003c\/p\u003e \u003cp\u003eWhat Educational Background Is Needed to Become a Predictive Modeler? 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Setting Up the Problem 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePredictive Analytics Processing Steps: CRISP-DM 19\u003c\/p\u003e \u003cp\u003eBusiness Understanding 21\u003c\/p\u003e \u003cp\u003eThe Three-Legged Stool 22\u003c\/p\u003e \u003cp\u003eBusiness Objectives 23\u003c\/p\u003e \u003cp\u003eDefining Data for Predictive Modeling 25\u003c\/p\u003e \u003cp\u003eDefining the Columns as Measures 26\u003c\/p\u003e \u003cp\u003eDefining the Unit of Analysis 27\u003c\/p\u003e \u003cp\u003eWhich Unit of Analysis? 28\u003c\/p\u003e \u003cp\u003eDefining the Target Variable 29\u003c\/p\u003e \u003cp\u003eTemporal Considerations for Target Variable 31\u003c\/p\u003e \u003cp\u003eDefining Measures of Success for Predictive Models 32\u003c\/p\u003e \u003cp\u003eSuccess Criteria for Classification 32\u003c\/p\u003e \u003cp\u003eSuccess Criteria for Estimation 33\u003c\/p\u003e \u003cp\u003eOther Customized Success Criteria 33\u003c\/p\u003e \u003cp\u003eDoing Predictive Modeling Out of Order 34\u003c\/p\u003e \u003cp\u003eBuilding Models First 34\u003c\/p\u003e \u003cp\u003eEarly Model Deployment 35\u003c\/p\u003e \u003cp\u003eCase Study: Recovering Lapsed Donors 35\u003c\/p\u003e \u003cp\u003eOverview 36\u003c\/p\u003e \u003cp\u003eBusiness Objectives 36\u003c\/p\u003e \u003cp\u003eData for the Competition 36\u003c\/p\u003e \u003cp\u003eThe Target Variables 36\u003c\/p\u003e \u003cp\u003eModeling Objectives 37\u003c\/p\u003e \u003cp\u003eModel Selection and Evaluation Criteria 38\u003c\/p\u003e \u003cp\u003eModel Deployment 39\u003c\/p\u003e \u003cp\u003eCase Study: Fraud Detection 39\u003c\/p\u003e \u003cp\u003eOverview 39\u003c\/p\u003e \u003cp\u003eBusiness Objectives 39\u003c\/p\u003e \u003cp\u003eData for the Project 40\u003c\/p\u003e \u003cp\u003eThe Target Variables 40\u003c\/p\u003e \u003cp\u003eModeling Objectives 41\u003c\/p\u003e \u003cp\u003eModel Selection and Evaluation Criteria 41\u003c\/p\u003e \u003cp\u003eModel Deployment 41\u003c\/p\u003e \u003cp\u003eSummary 42\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Data Understanding 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat the Data Looks Like 44\u003c\/p\u003e \u003cp\u003eSingle Variable Summaries 44\u003c\/p\u003e \u003cp\u003eMean 45\u003c\/p\u003e \u003cp\u003eStandard Deviation 45\u003c\/p\u003e \u003cp\u003eThe Normal Distribution 45\u003c\/p\u003e \u003cp\u003eUniform Distribution 46\u003c\/p\u003e \u003cp\u003eApplying Simple Statistics in Data Understanding 47\u003c\/p\u003e \u003cp\u003eSkewness 49\u003c\/p\u003e \u003cp\u003eKurtosis 51\u003c\/p\u003e \u003cp\u003eRank-Ordered Statistics 52\u003c\/p\u003e \u003cp\u003eCategorical Variable Assessment 55\u003c\/p\u003e \u003cp\u003eData Visualization in One Dimension 58\u003c\/p\u003e \u003cp\u003eHistograms 59\u003c\/p\u003e \u003cp\u003eMultiple Variable Summaries 64\u003c\/p\u003e \u003cp\u003eHidden Value in Variable Interactions: Simpson’s Paradox 64\u003c\/p\u003e \u003cp\u003eThe Combinatorial Explosion of Interactions 65\u003c\/p\u003e \u003cp\u003eCorrelations 66\u003c\/p\u003e \u003cp\u003eSpurious Correlations 66\u003c\/p\u003e \u003cp\u003eBack to Correlations 67\u003c\/p\u003e \u003cp\u003eCrosstabs 68\u003c\/p\u003e \u003cp\u003eData Visualization, Two or Higher Dimensions 69\u003c\/p\u003e \u003cp\u003eScatterplots 69\u003c\/p\u003e \u003cp\u003eAnscombe’s Quartet 71\u003c\/p\u003e \u003cp\u003eScatterplot Matrices 75\u003c\/p\u003e \u003cp\u003eOverlaying the Target Variable in Summary 76\u003c\/p\u003e \u003cp\u003eScatterplots in More Than Two Dimensions 78\u003c\/p\u003e \u003cp\u003eThe Value of Statistical Significance 80\u003c\/p\u003e \u003cp\u003ePulling It All Together into a Data Audit 81\u003c\/p\u003e \u003cp\u003eSummary 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Data Preparation 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eVariable Cleaning 84\u003c\/p\u003e \u003cp\u003eIncorrect Values 84\u003c\/p\u003e \u003cp\u003eConsistency in Data Formats 85\u003c\/p\u003e \u003cp\u003eOutliers 85\u003c\/p\u003e \u003cp\u003eMultidimensional Outliers 89\u003c\/p\u003e \u003cp\u003eMissing Values 90\u003c\/p\u003e \u003cp\u003eFixing Missing Data 91\u003c\/p\u003e \u003cp\u003eFeature Creation 98\u003c\/p\u003e \u003cp\u003eSimple Variable Transformations 98\u003c\/p\u003e \u003cp\u003eFixing Skew 99\u003c\/p\u003e \u003cp\u003eBinning Continuous Variables 103\u003c\/p\u003e \u003cp\u003eNumeric Variable Scaling 104\u003c\/p\u003e \u003cp\u003eNominal Variable Transformation 107\u003c\/p\u003e \u003cp\u003eOrdinal Variable Transformations 108\u003c\/p\u003e \u003cp\u003eDate and Time Variable Features 109\u003c\/p\u003e \u003cp\u003eZIP Code Features 110\u003c\/p\u003e \u003cp\u003eWhich Version of a Variable Is Best? 110\u003c\/p\u003e \u003cp\u003eMultidimensional Features 112\u003c\/p\u003e \u003cp\u003eVariable Selection Prior to Modeling 117\u003c\/p\u003e \u003cp\u003eSampling 123\u003c\/p\u003e \u003cp\u003eExample: Why Normalization Matters for K-Means Clustering 139\u003c\/p\u003e \u003cp\u003eSummary 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Itemsets and Association Rules 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTerminology 146\u003c\/p\u003e \u003cp\u003eCondition 147\u003c\/p\u003e \u003cp\u003eLeft-Hand-Side, Antecedent(s) 148\u003c\/p\u003e \u003cp\u003eRight-Hand-Side, Consequent, Output, Conclusion 148\u003c\/p\u003e \u003cp\u003eRule (Item Set) 148\u003c\/p\u003e \u003cp\u003eSupport 149\u003c\/p\u003e \u003cp\u003eAntecedent Support 149\u003c\/p\u003e \u003cp\u003eConfidence, Accuracy 150\u003c\/p\u003e \u003cp\u003eLift 150\u003c\/p\u003e \u003cp\u003eParameter Settings 151\u003c\/p\u003e \u003cp\u003eHow the Data Is Organized 151\u003c\/p\u003e \u003cp\u003eStandard Predictive Modeling Data Format 151\u003c\/p\u003e \u003cp\u003eTransactional Format 152\u003c\/p\u003e \u003cp\u003eMeasures of Interesting Rules 154\u003c\/p\u003e \u003cp\u003eDeploying Association Rules 156\u003c\/p\u003e \u003cp\u003eVariable Selection 157\u003c\/p\u003e \u003cp\u003eInteraction Variable Creation 157\u003c\/p\u003e \u003cp\u003eProblems with Association Rules 158\u003c\/p\u003e \u003cp\u003eRedundant Rules 158\u003c\/p\u003e \u003cp\u003eToo Many Rules 158\u003c\/p\u003e \u003cp\u003eToo Few Rules 159\u003c\/p\u003e \u003cp\u003eBuilding Classification Rules from Association Rules 159\u003c\/p\u003e \u003cp\u003eSummary 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Descriptive Modeling 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Preparation Issues with Descriptive Modeling 164\u003c\/p\u003e \u003cp\u003ePrincipal Component Analysis 165\u003c\/p\u003e \u003cp\u003eThe PCA Algorithm 165\u003c\/p\u003e \u003cp\u003eApplying PCA to New Data 169\u003c\/p\u003e \u003cp\u003ePCA for Data Interpretation 171\u003c\/p\u003e \u003cp\u003eAdditional Considerations before Using PCA 172\u003c\/p\u003e \u003cp\u003eThe Effect of Variable Magnitude on PCA Models 174\u003c\/p\u003e \u003cp\u003eClustering Algorithms 177\u003c\/p\u003e \u003cp\u003eThe K-Means Algorithm 178\u003c\/p\u003e \u003cp\u003eData Preparation for K-Means 183\u003c\/p\u003e \u003cp\u003eSelecting the Number of Clusters 185\u003c\/p\u003e \u003cp\u003eThe Kohonen SOM Algorithm 192\u003c\/p\u003e \u003cp\u003eVisualizing Kohonen Maps 194\u003c\/p\u003e \u003cp\u003eSimilarities with K-Means 196\u003c\/p\u003e \u003cp\u003eSummary 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Interpreting Descriptive Models 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStandard Cluster Model Interpretation 199\u003c\/p\u003e \u003cp\u003eProblems with Interpretation Methods 202\u003c\/p\u003e \u003cp\u003eIdentifying Key Variables in Forming Cluster Models 203\u003c\/p\u003e \u003cp\u003eCluster Prototypes 209\u003c\/p\u003e \u003cp\u003eCluster Outliers 210\u003c\/p\u003e \u003cp\u003eSummary 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Predictive Modeling 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDecision Trees 214\u003c\/p\u003e \u003cp\u003eThe Decision Tree Landscape 215\u003c\/p\u003e \u003cp\u003eBuilding Decision Trees 218\u003c\/p\u003e \u003cp\u003eDecision Tree Splitting Metrics 221\u003c\/p\u003e \u003cp\u003eDecision Tree Knobs and Options 222\u003c\/p\u003e \u003cp\u003eReweighting Records: Priors 224\u003c\/p\u003e \u003cp\u003eReweighting Records: Misclassification Costs 224\u003c\/p\u003e \u003cp\u003eOther Practical Considerations for Decision Trees 229\u003c\/p\u003e \u003cp\u003eLogistic Regression 230\u003c\/p\u003e \u003cp\u003eInterpreting Logistic Regression Models 233\u003c\/p\u003e \u003cp\u003eOther Practical Considerations for Logistic Regression 235\u003c\/p\u003e \u003cp\u003eNeural Networks 240\u003c\/p\u003e \u003cp\u003eBuilding Blocks: The Neuron 242\u003c\/p\u003e \u003cp\u003eNeural Network Training 244\u003c\/p\u003e \u003cp\u003eThe Flexibility of Neural Networks 247\u003c\/p\u003e \u003cp\u003eNeural Network Settings 249\u003c\/p\u003e \u003cp\u003eNeural Network Pruning 251\u003c\/p\u003e \u003cp\u003eInterpreting Neural Networks 252\u003c\/p\u003e \u003cp\u003eNeural Network Decision Boundaries 253\u003c\/p\u003e \u003cp\u003eOther Practical Considerations for Neural Networks 253\u003c\/p\u003e \u003cp\u003eK-Nearest Neighbor 254\u003c\/p\u003e \u003cp\u003eThe k-NN Learning Algorithm 254\u003c\/p\u003e \u003cp\u003eDistance Metrics for k-NN 258\u003c\/p\u003e \u003cp\u003eOther Practical Considerations for k-NN 259\u003c\/p\u003e \u003cp\u003eNaïve Bayes 264\u003c\/p\u003e \u003cp\u003eBayes’ Theorem 264\u003c\/p\u003e \u003cp\u003eThe Naïve Bayes Classifier 268\u003c\/p\u003e \u003cp\u003eInterpreting Naïve Bayes Classifiers 268\u003c\/p\u003e \u003cp\u003eOther Practical Considerations for Naïve Bayes 269\u003c\/p\u003e \u003cp\u003eRegression Models 270\u003c\/p\u003e \u003cp\u003eLinear Regression 271\u003c\/p\u003e \u003cp\u003eLinear Regression Assumptions 274\u003c\/p\u003e \u003cp\u003eVariable Selection in Linear Regression 276\u003c\/p\u003e \u003cp\u003eInterpreting Linear Regression Models 278\u003c\/p\u003e \u003cp\u003eUsing Linear Regression for Classification 279\u003c\/p\u003e \u003cp\u003eOther Regression Algorithms 280\u003c\/p\u003e \u003cp\u003eSummary 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Assessing Predictive Models 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBatch Approach to Model Assessment 284\u003c\/p\u003e \u003cp\u003ePercent Correct Classification 284\u003c\/p\u003e \u003cp\u003eRank-Ordered Approach to Model Assessment 293\u003c\/p\u003e \u003cp\u003eAssessing Regression Models 301\u003c\/p\u003e \u003cp\u003eSummary 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Model Ensembles 307\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMotivation for Ensembles 307\u003c\/p\u003e \u003cp\u003eThe Wisdom of Crowds 308\u003c\/p\u003e \u003cp\u003eBias Variance Tradeoff 309\u003c\/p\u003e \u003cp\u003eBagging 311\u003c\/p\u003e \u003cp\u003eBoosting 316\u003c\/p\u003e \u003cp\u003eImprovements to Bagging and Boosting 320\u003c\/p\u003e \u003cp\u003eRandom Forests 320\u003c\/p\u003e \u003cp\u003eStochastic Gradient Boosting 321\u003c\/p\u003e \u003cp\u003eHeterogeneous Ensembles 321\u003c\/p\u003e \u003cp\u003eModel Ensembles and Occam’s Razor 323\u003c\/p\u003e \u003cp\u003eInterpreting Model Ensembles 323\u003c\/p\u003e \u003cp\u003eSummary 326\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Text Mining 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMotivation for Text Mining 328\u003c\/p\u003e \u003cp\u003eA Predictive Modeling Approach to Text Mining 329\u003c\/p\u003e \u003cp\u003eStructured vs. Unstructured Data 329\u003c\/p\u003e \u003cp\u003eWhy Text Mining Is Hard 330\u003c\/p\u003e \u003cp\u003eText Mining Applications 332\u003c\/p\u003e \u003cp\u003eData Sources for Text Mining 333\u003c\/p\u003e \u003cp\u003eData Preparation Steps 333\u003c\/p\u003e \u003cp\u003ePOS Tagging 333\u003c\/p\u003e \u003cp\u003eTokens 336\u003c\/p\u003e \u003cp\u003eStop Word and Punctuation Filters 336\u003c\/p\u003e \u003cp\u003eCharacter Length and Number Filters 337\u003c\/p\u003e \u003cp\u003eStemming 337\u003c\/p\u003e \u003cp\u003eDictionaries 338\u003c\/p\u003e \u003cp\u003eThe Sentiment Polarity Movie Data Set 339\u003c\/p\u003e \u003cp\u003eText Mining Features 340\u003c\/p\u003e \u003cp\u003eTerm Frequency 341\u003c\/p\u003e \u003cp\u003eInverse Document Frequency 344\u003c\/p\u003e \u003cp\u003eTf-idf 344\u003c\/p\u003e \u003cp\u003eCosine Similarity 346\u003c\/p\u003e \u003cp\u003eMulti-Word Features: N-Grams 346\u003c\/p\u003e \u003cp\u003eReducing Keyword Features 347\u003c\/p\u003e \u003cp\u003eGrouping Terms 347\u003c\/p\u003e \u003cp\u003eModeling with Text Mining Features 347\u003c\/p\u003e \u003cp\u003eRegular Expressions 349\u003c\/p\u003e \u003cp\u003eUses of Regular Expressions in Text Mining 351\u003c\/p\u003e \u003cp\u003eSummary 352\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Model Deployment 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGeneral Deployment Considerations 354\u003c\/p\u003e \u003cp\u003eDeployment Steps 355\u003c\/p\u003e \u003cp\u003eSummary 375\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Case Studies 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSurvey Analysis Case Study: Overview 377\u003c\/p\u003e \u003cp\u003eBusiness Understanding: Defining the Problem 378\u003c\/p\u003e \u003cp\u003eData Understanding 380\u003c\/p\u003e \u003cp\u003eData Preparation 381\u003c\/p\u003e \u003cp\u003eModeling 385\u003c\/p\u003e \u003cp\u003eDeployment: “What-If” Analysis 391\u003c\/p\u003e \u003cp\u003eRevisit Models 392\u003c\/p\u003e \u003cp\u003eDeployment 401\u003c\/p\u003e \u003cp\u003eSummary and Conclusions 401\u003c\/p\u003e \u003cp\u003eHelp Desk Case Study 402\u003c\/p\u003e \u003cp\u003eData Understanding: Defining the Data 403\u003c\/p\u003e \u003cp\u003eData Preparation 403\u003c\/p\u003e \u003cp\u003eModeling 405\u003c\/p\u003e \u003cp\u003eRevisit Business Understanding 407\u003c\/p\u003e \u003cp\u003eDeployment 409\u003c\/p\u003e \u003cp\u003eSummary and Conclusions 411\u003c\/p\u003e \u003cp\u003eIndex 413\u003c\/p\u003e  \u003cp\u003e\"This book provides an excellent background to predictive analytics\" (\u003cem\u003eBCS,\u003c\/em\u003e December 2014)   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDEAN ABBOTT\u003c\/b\u003e is President of Abbott Analytics, Inc. (San Diego). He is an internationally recognized data mining and predictive analytics expert with over two decades experience in fraud detection, risk modeling, text mining, personality assessment, planned giving, toxicology, and other applications. He is also Chief Scientist of SmarterRemarketer, a company focusing on behaviorally- and data-driven marketing and web analytics.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAPPLY THE RIGHT ANALYTIC TECHNIQUE\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eApplied Predictive Analytics: Principles and Techniques for the Professional Data Analyst shows tech-savvy business managers and data analysts how to use predictive analytics to solve practical business problems. It teaches readers the methods, principles, and techniques for conducting predictive analytics projects, from start to finish. Internationally recognized data mining and predictive analytics expert Dean Abbott provides a practical and authoritative guide to best practices for successful predictive modeling, including expert tips and tricks to avoid common pitfalls.\u003c\/p\u003e \u003cp\u003eThis book explains the theory behind the principles of predictive analytics in plain English; readers don’t need an extensive background in math and statistics, which makes it ideal for most tech-savvy business and data analysts. Each of the chapters describes one or more specific techniques and how they relate to the overall process model for predictive analytics. The depth of the description of a technique will match the complexity of the approach, with the intent to describe the techniques in enough depth for a practitioner to understand the effect of the major parameters needed to effectively use the technique and interpret the results.\u003c\/p\u003e \u003cp\u003eEach of the techniques is illustrated by examples, either unique to the task or as part of predictive modeling competitions. The companion website will provide all of the data sets used to generate these examples, along with links to open source and commercial software, so that readers can recreate and explore the examples.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eWith detailed descriptions of techniques that get results, \u003ci\u003eApplied Predictive Analytics\u003c\/i\u003e shows you how to:\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003e\u003cb\u003eChoose the proper analytics technique for various scenarios\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eAvoid common mistakes and identify the weaknesses of various techniques\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eMitigate outliers and fill in missing data when necessary\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eInterpret predictive models often considered “black boxes,” including model ensembles\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eLearn how to assess model performance so the best model is selected\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eApply the appropriate sampling techniques for building and updating models\u003c\/b\u003e\u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988753006821,"sku":"NP9781118727966","price":50.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118727966.jpg?v=1761781454","url":"https:\/\/k12savings.com\/es\/products\/applied-predictive-analytics-isbn-9781118727966","provider":"K12savings","version":"1.0","type":"link"}