IBM SPSS modeler cookbook

In Detail IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide. Approach This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts. It is loaded with step-by-step examples explaining the process followed by the experts. Who this book is for If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics.

Includes index.

Cover -- Copyright -- Credits -- Foreword -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Data Understanding -- Introduction -- Using an empty aggregate to evaluate sample size -- Evaluating the need to sample from the initial data -- Using CHAID stumps when interviewing an SME -- Using a single cluster K-means as an alternative to anomaly detection -- Using an @NULL multiple Derive to explore missing data -- Creating an outlier report to give to SMEs

""Detecting potential model instability early using the Partition node and Feature Selection""""Chapter 2: Data Preparation � Select""; ""Introduction""; ""Using the Feature Selection node creatively to remove, or decapitate, perfect predictors""; ""Running a Statistics node on anti-join to evaluate potential missing data""; ""Evaluating the use of sampling for speed""; ""Removing redundant variables using correlation matrices""; ""Selecting variable using the CHAID modeling node""; ""Selecting variables using the Means node""

""Selecting variables using single-antecedent association rules""""Chapter 3: Data Preparation â€? Clean""; ""Introduction""; ""Binning scale va…