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 Analytics -- Obstacles 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 Models -- Success 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 Criteria -- Model 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 Dimensions -- Scatterplots -- 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 -- Date and Time Variable Features -- ZIP Code Features -- Which Version of a Variable Is Best? -- Multidimensional Features -- Variable Selection Prior to Modeling -- Sampling -- Example: Why Normalization Matters for K-Means Clustering -- Summary -- Chapter 5 Itemsets and Association Rules -- Terminology -- Condition -- Left-Hand-Side, Antecedent(s) -- Right-Hand-Side, Consequent, Output, Conclusion -- Rule (Item Set) -- Support -- Antecedent Support -- Confidence, Accuracy -- Lift -- Parameter Settings.

How the Data Is Organized -- Standard Predictive Modeling Data Format -- Transactional Format -- Measures of Interesting Rules -- Deploying Association Rules -- Variable Selection -- Interaction Variable Creation -- Problems with Association Rules -- Redundant Rules -- Too Many Rules -- Too Few Rules -- Building Classification Rules from Association Rules -- Summary -- Chapter 6 Descriptive Modeling -- Data Preparation Issues with Descriptive Modeling -- Principal Component Analysis -- The PCA Algorithm -- Applying PCA to New Data -- PCA for Data Interpretation -- Additional Considerations before Using PCA -- The Effect of Variable Magnitude on PCA Models -- Clustering Algorithms -- The K-Means Algorithm -- Data Preparation for K-Means -- Selecting the Number of Clusters -- The Kohonen SOM Algorithm -- Visualizing Kohonen Maps -- Similarities with K-Means -- Summary -- Chapter 7 Interpreting Descriptive Models -- Standard Cluster Model Interpretation -- Problems with Interpretation Methods -- Identifying Key Variables in Forming Cluster Models -- Cluster Prototypes -- Cluster Outliers -- Summary -- Chapter 8 Predictive Modeling -- Decision Trees -- The Decision Tree Landscape -- Building Decision Trees -- Decision Tree Splitting Metrics -- Decision Tree Knobs and Options -- Reweighting Records: Priors -- Reweighting Records: Misclassification Costs -- Other Practical Considerations for Decision Trees -- Logistic Regression -- Interpreting Logistic Regression Models -- Other Practical Considerations for Logistic Regression -- Neural Networks -- Building Blocks: The Neuron -- Neural Network Training -- The Flexibility of Neural Networks -- Neural Network Settings -- Neural Network Pruning -- Interpreting Neural Networks -- Neural Network Decision Boundaries.

Other Practical Considerations for Neural Networks -- K-Nearest Neighbor -- The k-NN Learning Algorithm -- Distance Metrics for k-NN -- Other Practical Considerations for k-NN -- Naïve Bayes -- Bayes' Theorem -- The Naïve Bayes Classifier -- Interpreting Naïve Bayes Classifiers -- Other Practical Considerations for Naïve Bayes -- Regression Models -- Linear Regression -- Linear Regression Assumptions -- Variable Selection in Linear Regression -- Interpreting Linear Regression Models -- Using Linear Regression for Classification -- Other Regression Algorithms -- Summary -- Chapter 9 Assessing Predictive Models -- Batch Approach to Model Assessment -- Percent Correct Classification -- Rank-Ordered Approach to Model Assessment -- Assessing Regression Models -- Summary -- Chapter 10 Model Ensembles -- Motivation for Ensembles -- The Wisdom of Crowds -- Bias Variance Tradeoff -- Bagging -- Boosting -- Improvements to Bagging and Boosting -- Random Forests -- Stochastic Gradient Boosting -- Heterogeneous Ensembles -- 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 -- Text Mining Applications -- Data Sources for Text Mining -- Data Preparation Steps -- POS Tagging -- Tokens -- Stop Word and Punctuation Filters -- Character Length and Number Filters -- Stemming -- Dictionaries -- The Sentiment Polarity Movie Data Set -- Text Mining Features -- Term Frequency -- Inverse Document Frequency -- TF-IDF -- Cosine Similarity -- Multi-Word Features: N-Grams -- Reducing Keyword Features -- Grouping Terms -- Modeling with Text Mining Features -- Regular Expressions -- Uses of Regular Expressions in Text Mining -- Summary.

Chapter 12 Model Deployment -- General Deployment Considerations -- Deployment Steps -- Summary -- Chapter 13 Case Studies -- Survey Analysis Case Study: Overview -- Business Understanding: Defining the Problem -- Data Understanding -- Data Preparation -- Modeling -- Deployment: "What-If" Analysis -- Revisit Models -- Deployment -- Summary and Conclusions -- Help Desk Case Study -- Data Understanding: Defining the Data -- Data Preparation -- Modeling -- Revisit Business Understanding -- Deployment -- Summary and Conclusions -- Index.