Propensities and Cutoff for Classification 112Ĭhanging the Cutoff Values for a Confussion Matrix in JMP 114 Using Principal Components for Classification and Prediction 100Ĥ.9 Dimension Reduction Using Regression Models 100Ĥ.10 Dimension Reduction Using Classification and Regression Trees 100ĥ.2 Evaluating Predictive Performance 106Ĭomparing Training and Validation Performance 108 Visualizing Hierarchical Data: More on Treemaps 75ģ.6 Summary of Major Visualizations and Operations, According to DataĤ.6 Reducing the Number of Categories in Categorical Variables 87Ĥ.7 Converting a Categorical Variable to a Continuous Variable 90 Multivariate Plot: Parallel Coordinates Plot 71 Manipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering 65Īdding Trendlines in the Graph Builder 69 Heatmaps (Color Maps and Cell Plots): Visualizing Correlations and Missing Values 59Īdding Variables: Color, Size, Shape, Multiple Panels, and Animation 62 PART II DATA EXPLORATION AND DIMENSION REDUCTIONģ.3 Basic Charts: Bar Charts, Line Graphs, and Scatterplots 54ĭistribution Plots: Boxplots and Histograms 56 Predicting Home Values in a Boston Neighborhood 29ĭata Mining Software Tools: the State of theMarket by Herb Edelstein 41 Partitioning Data for Crossvalidation in JMP Pro 27Ģ.6 Building a Predictive Model with JMP Pro 29 Oversampling Rare Events in Classification Tasks 19 Using JMP Pro, Statistical Discovery Software from SAS 11Īssociation Rules and Recommendation Systems 15ĭata Reduction and Dimension Reduction 16 The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field.ġ.6 Why Are There So Many Different Methods? 7 ![]() A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics.Data-rich case studies to illustrate various applications of data mining techniques.End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material.Detailed summaries that supply an outline of key topics at the beginning of each chapter.Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting.ĭata Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining.įeaturing hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book
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