eBook - ePub
IBM SPSS Modeler Cookbook
- 382 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
IBM SPSS Modeler Cookbook
About this book
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.
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Information
IBM SPSS Modeler Cookbook
Table of Contents
IBM SPSS Modeler Cookbook
Credits
Foreword
About the Authors
About the Reviewers
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Preface
What is CRISP-DM?
Data mining is a business process
The IBM SPSS Modeler workbench
A brief history of the Clementine workbench
Historical introduction to scripting
What this book covers
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Data Understanding
Introduction
Using an empty aggregate to evaluate sample size
Getting ready
How to do it...
How it works...
There's more...
A modified version
See also
Evaluating the need to sample from the initial data
Getting ready
How to do it...
How it works...
There's more...
See also
Using CHAID stumps when interviewing an SME
Getting ready
How to do it...
How it works...
See also
Using a single cluster K-means as an alternative to anomaly detection
Getting ready
How to do it...
How it works...
There's more...
Using an @NULL multiple Derive to explore missing data
Getting ready
How to do it...
How it works...
See also
Creating an Outlier report to give to SMEs
Getting ready
How to do it...
How it works...
See also
Detecting potential model instability early using the Partition node and Feature Selection node
Getting ready
How to do it...
How it works...
See also
2. Data Preparation – Select
Introduction
Using the Feature Selection node creatively to remove or decapitate perfect predictors
Getting ready
How to do it...
How it works...
There's more...
See also
Running a Statistics node on anti-join to evaluate the potential missing data
Getting ready
How to do it...
How it works...
See also
Evaluating the use of sampling for speed
Getting ready
How to do it...
How it works...
There's more...
See also
Removing redundant variables using correlation matrices
Getting ready
How to do it...
How it works...
There's more...
See also
Selecting variables using the CHAID Modeling node
Getting ready
How to do it...
How it works...
There's more...
See also
Selecting variables using the Means node
Getting ready
How to do it...
How it works...
There's more...
See also
Selecting variables using single-antecedent Association Rules
Getting ready
How to do it...
How it works...
There's more...
See also
3. Data Preparation – Clean
Introduction
Binning scale variables to address missing data
Getting ready
How to do it...
How it works...
See also
Using a full data model/partial data model approach to address missing data
Getting ready
How to do it...
How it works...
There's more...
See also
Imputing in-stream mean or median
Getting ready
How to do it...
How it works...
There's more...
See also
Imputing missing values randomly from uniform or normal distributions
Getting ready
How to do it...
How it works...
There's more...
See also
Using random imputation to match a variable's distribution
Getting ready
How to do it...
How it works...
There's more...
See also
Searching for similar records using a Neural Network for inexact matching
Getting ready
How to do it...
How it works...
There's more...
See also
Using neuro-fuzzy searching to find similar names
Getting ready
How to do it...
How it works...
There's more...
See also
Producing longer Soundex codes
Getting ready
How to do it...
How it works...
There's more...
See also
4. Data Preparation – Construct
Introduction
Building transformations with multiple Derive nodes
Getting ready
How to do it...
How it works...
There's more...
Calculating and comparing conversion rates
Getting ready
How to do it...
How it works...
There's more...
See also
Grouping categorical values
Getting ready
How to do it...
How it works...
There's more...
Transforming high skew and kurtosis variables with a multiple Derive node
Getting ready
How to do it...
How it works...
There's more...
Creating flag variables for aggregation
Getting ready
How to do it...
How it works...
There's more...
Using Association Rules for interaction detection/feature creation
Getting ready
How to do it...
How it works...
There's more...
Creating time-aligned cohorts
Getting ready
How to do it...
How it works...
There's more...
5. Data Preparation – Integrate and Format
Introduction
Speeding up merge with caching and optimization settings
Getting ready
How to do it...
How it works...
See also
Merging a lookup table
Getting ready
How to do it...
How it works...
See also
Shuffle-down (nonstandard aggregation)
Getting ready
How to do it...
How it works...
There's more...
See also
Cartesian product merge using key-less merge by key
Getting ready
How to do it...
How it works...
There's more...
See also
Multiplying out using Cartesian product merge, user source, and derive dummy
Getting ready
How to do it...
How it works...
There's more...
See also
Changing large numbers of variable names without scripting
Getting ready
How to do it...
How it works...
There's more...
See also
Parsing nonstandard dates
Getting ready
How to do it...
How it works...
There's more...
Nesting functions into one Derive node
Performing clean downstream of a calculation using a Filter node
Using parameter...
Table of contents
- IBM SPSS Modeler Cookbook
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Yes, you can access IBM SPSS Modeler Cookbook by Keith McCormick, Dean Abbott, Meta S. Brown, Tom Khabaza, Scott R. Mutchler in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Mining. We have over one million books available in our catalogue for you to explore.