product iamge

IBM SPSS Modeler cookbook

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Download
Search on Amazon

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

Over 60 practical recipes to achieve better results using the experts' methods for data mining

Overview

  • Go beyond mere insight and build models than you can deploy in the day to day running of your business
  • Save time and effort while getting more value from your data than ever before
  • Loaded with detailed step-by-step examples that show you exactly how it’s done by the best in the business

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.

What you will learn from this book

  • Use and understand the industry standard CRISP_DM process for data mining.
  • Assemble data simply, quickly, and correctly using the full power of extraction, transformation, and loading (ETL) tools.
  • Control the amount of time you spend organizing and formatting your data.
  • Develop predictive models that stand up to the demands of real-life applications.
  • Take your modeling to the next level beyond default settings and learn the tips that the experts use.
  • Learn why the best model is not always the most accurate one.
  • Master deployment techniques that put your discoveries to work making the most of your business’ most critical resources.
  • Challenge yourself with scripting for ultimate control and automation - it’s easier than you think!

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.

Author(s): McCormick, Keith
Publisher: Packt Publishing
Year: 2013

Language: English
Pages: 382
City: Birmingham, UK
Tags: Data mining.;SPSS (Computer file);COMPUTERS;General.

Content: 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 --
SelectIntroduction
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 --
Clean
Introduction
Binning scale variables to address missing data. Using a full data model/partial data model approach to address missing dataImputing 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
Chapter 4: 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 nodeCreating flag variables for aggregation
Using Association Rules for interaction detection/feature creation
Creating time-aligned cohorts
Chapter 5: Data Preparation --
Integrate and Format
Introduction
Speeding up merge with caching and optimization settings
Merging a look-up 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 datesParsing and performing a conversion on a complex stream
Sequence processing
Chapter 6: Selecting and Building a Model
Introduction
Evaluating balancing with the Auto Classifier
Building models with and without outliers
Neural Network 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
Chapter 7: Modeling --
Assessment, Evaluation, Deployment, and Monitoring
Introduction
How (and why) to validate as well as test.