Data mining for business analytics : concepts, techniques, and applications with JMP Pro /: concepts, techniques, and applications with JMP Pro. (2016)

• Record Type: Book
• Further Information: Note: Galit Shmueli, Peter C. Bruce, Mia L. Stephens, Nitin R. Patel.
• Authors: Shmueli, Galit, 1971-; Bruce, Peter C; Stephens, Mia L; Patel, Nitin R
• Contents: Dedication i Foreword xvii Preface xviii Acknowledgments xx P ART I PRELIMINARIES C HAPTER 1 Introduction 3 1.1 What is Business Analytics? 3 1.2 What is Data Mining? 5 1.3 Data Mining and Related Terms 5 1.4 Big Data 6 1.5 Data science 7 1.6 Why Are There So Many Different Methods? 8 1.7 Terminology and Notation 9 1.8 Road Maps to This Book 11 Order of Topics 12 C HAPTER 2 Overview of the Data Mining Process 15 2.1 Introduction 15 2.2 Core Ideas in Data Mining 16 2.3 The Steps in Data Mining 19 2.4 Preliminary Steps 20 2.5 Predictive Power and Overfitting 28 2.6 Building a Predictive Model with JMP Pro 33 2.7 Using JMP Pro for Data Mining 42 2.8 Automating Data Mining Solutions 42 Data Mining Software Tools (Herb Edelstein) 44 Problems 47 P ART II DATA EXPLORATION AND DIMENSION REDUCTION C HAPTER 3 Data Visualization 52 3.1 Uses of Data Visualization 52 3.2 Data Examples 54 Example 1: Boston Housing Data 54 Example 2: Ridership on Amtrak Trains 55 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatterplots 55 Distribution Plots 58 Heatmaps: visualizing correlations and missing values 61 3.4 Multi-Dimensional Visualization 63 Adding Variables: Color, Hue, Size, Shape, Multiple Panels, Animation 63 Manipulations: Re-scaling, Aggregation and Hierarchies, Zooming and Panning, Filtering 67 Reference: Trend Line and Labels 70 Scaling Up: Large Datasets 72 Multivariate Plot: Parallel Coordinates Plot 73 Interactive Visualization 74 3.5 Specialized Visualizations 76 Visualizing Networked Data 76 Visualizing Hierarchical Data: Treemaps 77 Visualizing Geographical Data: Maps 78 3.6 Summary of Major Visualizations and Operations, According to Data Mining Goal 80 Prediction 80 Classification 81 Time Series Forecasting 81 Unsupervised Learning 82 Problems 83 C HAPTER 4 Dimension Reduction 85 4.1 Introduction 85 4.2 Curse of Dimensionality 86 4.3 Practical Considerations 86 Example 1: House Prices in Boston 87 4.4 Data Summaries 88 4.5 Correlation Analysis 91 4.6 Reducing the Number of Categories in Categorical Variables 92 4.7 Converting A Categorical Variable to A Continuous Variable 94 4.8 Principal Components Analysis 94 Example 2: Breakfast Cereals 95 Principal Components 101 Normalizing the Data 102 Using Principal Components for Classification and Prediction 104 4.9 Dimension Reduction Using Regression Models 104 4.10 Dimension Reduction Using Classification and Regression Trees 106 Problems 107 P ART III PERFORMANCE EVALUATION C HAPTER 5 Evaluating Predictive Performance 111 5.1 Introduction 111 5.2 Evaluating Predictive Performance 112 Benchmark: The Average 112 Prediction Accuracy Measures 113 5.3 Judging Classifier Performance 115 Benchmark: The Naive Rule 115 Class Separation 115 The Classification Matrix 116 Using the Validation Data 117 Accuracy Measures 117 Cutoff for Classification 118 Performance in Unequal Importance of Classes 122 Asymmetric Misclassification Costs 123 5.4 Judging Ranking Performance 127 5.5 Oversampling 131 Problems 138 P ART IV PREDICTION AND CLASSIFICATION METHODS C HAPTER 6 Multiple Linear Regression 141 6.1 Introduction 141 6.2 Explanatory vs. Predictive Modeling 142 6.3 Estimating the Regression Equation and Prediction 143 Example: Predicting the Price of Used Toyota Corolla Automobiles . 144 6.4 Variable Selection in Linear Regression 149 Reducing the Number of Predictors 149 How to Reduce the Number of Predictors 150 Manual Variable Selection 151 Automated Variable Selection 151 Problems 160 C HAPTER 7 k -Nearest Neighbors ( k NN) 165 7.1 The k-NN Classifier (categorical outcome) 165 Determining Neighbors 165 Classification Rule 166 Example: Riding Mowers 166 Choosing k 167 Setting the Cutoff Value 169 7.2 k-NN for a Numerical Response 171 7.3 Advantages and Shortcomings of k-NN Algorithms 172 Problems 174 C HAPTER 8 The Naive Bayes Classifier 176 8.1 Introduction 176 Example 1: Predicting Fraudulent Financial Reporting 177 8.2 Applying the Full (Exact) Bayesian Classifier 178 8.3 Advantages and Shortcomings of the Naive Bayes Classifier 187 Advantages and Shortcomings of the naive Bayes Classifier 187 Problems 191 C HAPTER 9 Classification and Regression Trees 194 9.1 Introduction 194 9.2 Classification Trees 195 Example 1: Riding Mowers 196 9.3 Growing a Tree 198 Growing a Tree Example 198 Growing a Tree with CART 203 9.4 Evaluating the Performance of a Classification Tree 203 Example 2: Acceptance of Personal Loan 203 9.5 Avoiding Overfitting 204 Stopping Tree Growth: CHAID 205 Pruning the Tree 207 9.6 Classification Rules from Trees 208 9.7 Classification Trees for More Than two Classes 210 9.8 Regression Trees 210 Prediction 213 Evaluating Performance 214 9.9 Advantages and Weaknesses of a Tree 214 9.10 Improving Prediction: Multiple Trees 216 9.11 CART, and Measures of Impurity 218 Measuring Impurity 218 Problems 221 C HAPTER 10 Logistic Regression 224 10.1 Introduction 224 10.2 The Logistic Regression Model 226 Example: Acceptance of Personal Loan 227 Model with a Single Predictor 229 Estimating the Logistic Model from Data: Computing Parameter Estimates 231 10.3 Evaluating Classification Performance 234 Variable Selection 236 10.4 Example of Complete Analysis: Predicting Delayed Flights 237 Data Preprocessing 240 Model Fitting, Estimation and Interpretation - A Simple Model 240 Model Fitting, Estimation and Interpretation - The Full Model 241 Model Performance 243 Variable Selection 245 10.5 Appendix: Logistic Regression for Profiling 249 Appendix A: Why Linear Regression Is Inappropriate for a Categorical Response 249 Appendix B: Evaluating Explanatory Power 250 Appendix C: Logistic Regression for More Than Two Classes 253 Problems 257 C HAPTER 11 Neural Nets
• Edition: 1st
• Publisher Details: Hoboken, New Jersey : John Wiley & Sons, Inc
• Publication Date: 2016
• Extent: 1 online resource
• Subjects: 006.312; Business mathematics -- Computer programs; Business -- Data processing; Data mining
• Languages: English
• ISBNs: 9781118877524; 9781118956625
• Related ISBNs: 9781118877432
• Notes: Note: Description based on CIP data; item not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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