IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

Overview of this book

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.

IBM SPSS Modeler Cookbook

Credits

Foreword

About the Authors

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

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 node

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 the potential missing data

Evaluating the use of sampling for speed

Removing redundant variables using correlation matrices

Selecting variables using the CHAID Modeling node

Selecting variables using the Means node

Selecting variables using single-antecedent Association Rules

Data Preparation – Clean

Introduction

Binning scale variables to address missing data

Using a full data model/partial data model approach to address missing data

Imputing in-stream mean or median

Imputing missing values randomly from uniform or normal distributions

Using random imputation to match a variable's distribution

Searching for similar records using a Neural Network for inexact matching

Using neuro-fuzzy searching to find similar names

Producing longer Soundex codes

Data Preparation – Construct

Introduction

Building transformations with multiple Derive nodes

Calculating and comparing conversion rates

Grouping categorical values

Transforming high skew and kurtosis variables with a multiple Derive node

Creating flag variables for aggregation

Using Association Rules for interaction detection/feature creation

Creating time-aligned cohorts

Data Preparation – Integrate and Format

Introduction

Speeding up merge with caching and optimization settings

Merging a lookup table

Shuffle-down (nonstandard aggregation)

Cartesian product merge using key-less merge by key

Multiplying out using Cartesian product merge, user source, and derive dummy

Changing large numbers of variable names without scripting

Parsing nonstandard dates

Parsing and performing a conversion on a complex stream

Sequence processing

Selecting and Building a Model

Introduction

Evaluating balancing with Auto Classifier

Building models with and without outliers

Using Neural Network for Feature Selection

Creating a bootstrap sample

Creating bagged logistic regression models

Using KNN to match similar cases

Using Auto Classifier to tune models

Next-Best-Offer for large datasets

Modeling – Assessment, Evaluation, Deployment, and Monitoring

Introduction

How (and why) to validate as well as test

Using classification trees to explore the predictions of a Neural Network

Correcting a confusion matrix for an imbalanced target variable by incorporating priors

Using aggregate to write cluster centers to Excel for conditional formatting

Creating a classification tree financial summary using aggregate and an Excel Export node

Reformatting data for reporting with a Transpose node

Changing formatting of fields in a Table node

Combining generated filters

CLEM Scripting

Introduction

Building iterative Neural Network forecasts

Quantifying variable importance with Monte Carlo simulation

Implementing champion/challenger model management

Detecting outliers with the jackknife method

Optimizing K-means cluster solutions

Automating time series forecasts

Automating HTML reports and graphs

Rolling your own modeling algorithm – Weibull analysis

Business Understanding

Introduction

Define business objectives by Tom Khabaza

Assessing the situation by Meta Brown

Translating your business objective into a data mining objective by Dean Abbott

Produce a project plan – ensuring a realistic timeline by Keith McCormick

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

About the Authors

Keith McCormick is the Vice President and General Manager of QueBIT Consulting's Advanced Analytics team. He brings a wealth of consulting/training experience in statistics, predictive modeling and analytics, and data mining. For many years, he has worked in the SPSS community, first as an External Trainer and Consultant for SPSS Inc., then in a similar role with IBM, and now in his role with an award winning IBM partner. He possesses a BS in Computer Science and Psychology from Worcester Polytechnic Institute.

He has been using Stats software tools since the early 90s, and has been training since 1997. He has been doing data mining and using IBM SPSS Modeler since its arrival in North America in the late 90s. He is an expert in IBM's SPSS software suite including IBM SPSS Statistics, IBM SPSS Modeler (formally Clementine), AMOS, Text Mining, and Classification Trees. He is active as a moderator and participant in statistics groups online including LinkedIn's Statistics and Analytics Consultants Group. He also blogs and reviews related books at KeithMcCormick.com. He enjoys hiking in out of the way places, finding unusual souvenirs while traveling overseas, exotic foods, and old books.

I would like to thank my coauthors for stealing time from their busy careers to meet a need that the SPSS community had for some time. It wouldn't have been the same without all five of us and our diverse experiences.

Thanks to Colin for his humor, unique perspective, and generous comments. Thanks also for our cover photo!

Thanks to Jesus and Terry for going above and beyond in their role as reviewers. Both were guest chefs, contributing last minute recipes after the initial reviews came back. Without Jesus' considerable editing skills this would not have been a 2013 release.

Thanks finally to David Oh, whose prior publication experience, persistence, and energy kept the project going even when no one else was able to keep other responsibilities at bay.

Dean Abbott is the President of Abbott Analytics, Inc. in San Diego, California. He has over two decades experience in applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, customer acquisition and retention, digital behavior for web applications and mobile, customer lifetime value, survey analysis, donation solicitation and planned giving. He has developed, coded, and evaluated algorithms for use in commercial data mining and pattern recognition products, including polynomial networks, neural networks, radial basis functions, and clustering algorithms for multiple software vendors.

He is a seasoned instructor, having taught a wide range of data mining tutorials and seminars to thousands of attendees, including PAW, KDD, INFORMS, DAMA, AAAI, and IEEE conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. He also has taught both applied and hands-on data mining courses for major software vendors, including IBM SPSS Modeler, Statsoft STATISTICA, Salford System SPM, SAS Enterprise Miner, IBM PredictiveInsight, Tibco Spotfire Miner, KNIME, RapidMiner, and Megaputer Polyanalyst.

Meta S. Brown helps organizations use practical data analysis to solve everyday business problems. A hands-on analyst who has tackled projects with up to $900 million at stake, she is a recognized expert in cutting-edge business analytics.

She is devoted to educating the business community on effective use of statistics, data mining, and text mining. A sought-after analytics speaker, she has conducted over 4000 hours of seminars, attracting audiences across North America, Europe, and South America. Her articles appear frequently on All Analytics, Smart Data Collective, and other publications. She is also co-author of Big Data, Mining and Analytics: Key Components for Strategic Decisions (forthcoming from CRC Press, Editor: Stephan Kudyba).

She holds a Master of Science in Nuclear Engineering from the Massachusetts Institute of Technology, a Bachelor of Science in Mathematics from Rutgers University, and professional certifications from the American Society for Quality and National Association for Healthcare Quality. She has served on the faculties of Roosevelt University and National-Louis University.

Tom Khabaza is an independent consultant in predictive analytics and data mining, and the Founding Chairman of the Society of Data Miners. He is a data mining veteran of over 20 years and many industries and applications. He has helped to create the IBM SPSS Modeler (Clementine) data mining workbench and the industry standard CRISP-DM methodology, and led the first integrations of data mining and text mining. His recent thought leadership includes the 9 Laws of Data Mining.

Scott R. Mutchler is the Vice President of Advanced Analytics Services at QueBIT Consulting LLC. He had spent the first 17 years of his career building enterprise solutions as a DBA, software developer, and enterprise architect. When Scott discovered his true passion was for advanced analytics, he moved into advanced analytics leadership roles where he was able to drive millions of dollars in incremental revenues and cost savings through the application of advanced analytics to most challenging business problems. His strong IT background turned out to be a huge asset in building integrated advanced analytics solutions.

Recently, he was the Predictive Analytics Worldwide Industrial Sector Lead for IBM. In this role, he worked with IBM SPSS clients worldwide. He architected advanced analytic solutions for clients in some of the world's largest retailers and manufacturers.

He received his Masters from Virginia Tech in Geology. He stays in Colorado and enjoys an outdoor lifestyle, playing guitar, and travelling.

IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

Overview of this book

Related Content you might be interested in

Current Title:

IBM SPSS Modeler Cookbook

About the Authors