IBM SPSS Modeler essentials : effective techniques for building powerful data minng and predictive analytics solutions / Jesus Salcedo, Keith McCormick.

Format:

Book

Author/Creator:

Salcedo, Jesus, author.

McCormick, Keith, author.

Language:

English

Subjects (All):

SPSS (Computer file).

Social sciences--Statistical methods--Data processing.

Social sciences.

Data mining.

Physical Description:

1 online resource (218 pages) : illustrations (some color)

Edition:

1st edition

Other Title:

SPSS Modeler essentials

Statistical package for the social sciences Modeler essentials

Place of Publication:

Birmingham, England : Packt, 2017.

System Details:

text file

Summary:

Get to grips with the fundamentals of data mining and predictive analytics with IBM SPSS Modeler About This Book Get up?and-running with IBM SPSS Modeler without going into too much depth. Identify interesting relationships within your data and build effective data mining and predictive analytics solutions A quick, easy?to-follow guide to give you a fundamental understanding of SPSS Modeler, written by the best in the business Who This Book Is For This book is ideal for those who are new to SPSS Modeler and want to start using it as quickly as possible, without going into too much detail. An understanding of basic data mining concepts will be helpful, to get the best out of the book. What You Will Learn Understand the basics of data mining and familiarize yourself with Modeler's visual programming interface Import data into Modeler and learn how to properly declare metadata Obtain summary statistics and audit the quality of your data Prepare data for modeling by selecting and sorting cases, identifying and removing duplicates, combining data files, and modifying and creating fields Assess simple relationships using various statistical and graphing techniques Get an overview of the different types of models available in Modeler Build a decision tree model and assess its results Score new data and export predictions In Detail IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available. Since it is popular in corporate settings, widely available in university settings, and highly compatible with all the latest technologies, it is the perfect way to start your Data Science and Machine Learning journey. This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler's easy to learn ?visual programming? style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials. The authors have drawn upon their decades of teaching thousands of new users, to choose those aspects of Modeler that you should learn first, so that you get off to a good start using proven best practices. This bo...

Contents:

Cover

Copyright

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Dedication

Table of Contents

Preface

Chapter 1: Introduction to Data Mining and Predictive Analytics

Introduction to data mining

CRISP-DM overview

Business Understanding

Data Understanding

Data Preparation

Modeling

Evaluation

Deployment

Learning more about CRISP-DM

The data mining process (as a case study)

Summary

Chapter 2: The Basics of Using IBM SPSS Modeler

Introducing the Modeler graphic user interface

Stream canvas

Palettes

Modeler menus

Toolbar

Manager tabs

Project window

Building streams

Mouse buttons

Adding nodes

Editing nodes

Deleting nodes

Building a stream

Connecting nodes

Deleting connections

Modeler stream rules

Help options

Help menu

Dialog help

Chapter 3: Importing Data into Modeler

Data structure

Var. File source node

Var. File source node File tab

Var. File source node Data tab

Var. File source node Filter tab

Var. File source node Types tab

Var. File source node Annotations tab

Viewing data

Excel source node

Database source node

Levels of measurement and roles

Chapter 4: Data Quality and Exploration

Data Audit node options

Data Audit node results

The Quality tab

Missing data

Ways to address missing data

Defining missing values in the Type node

Imputing missing values with the Data Audit node

Chapter 5: Cleaning and Selecting Data

Selecting cases

Expression Builder

Sorting cases

Identifying and removing duplicate cases

Reclassifying categorical values

Chapter 6: Combining Data Files

Combining data files with the Append node

Removing fields with the Filter node.

Combining data files with the Merge node

The Filter tab

The Optimization tab

Chapter 7: Deriving New Fields

Derive - Formula

Derive - Flag

Derive - Nominal

Derive - Conditional

Chapter 8: Looking for Relationships Between Fields

Relationships between categorical fields

Distribution node

Matrix node

Relationships between categorical and continuous fields

Histogram node

Means node

Relationships between continuous fields

Plot node

Statistics node

Chapter 9: Introduction to Modeling Options in IBM SPSS Modeler

Classification

Categorical targets

Numeric targets

The Auto nodes

Data reduction modeling nodes

Association

Segmentation

Choosing between models

Chapter 10: Decision Tree Models

Decision tree theory

CHAID theory

How CHAID processes different types of input variables

Stopping rules

Building a CHAID Model

Partition node

Overfitting

CHAID dialog options

CHAID results

Chapter 11: Model Assessment and Scoring

Contrasting model assessment with the Evaluation phase

Model assessment using the Analysis node

Modifying CHAID settings

Model comparison using the Analysis node

Model assessment and comparison using the Evaluation node

Scoring new data

Exporting predictions

Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed February 5, 2018).

ISBN:

9781788296823

1788296826

OCLC:

1021887713