IBM SPSS Modeler Essentials: Effective techniques for building powerful data mining and predictive analytics solutions

IBM SPSS Modeler Essentials: Effective techniques for building powerful data mining and predictive analytics solutions

By 作者: Jesus Salcedo – Keith McCormick

ISBN-10 书号: 1788291115
ISBN-13 书号: 9781788291118
Release Finelybook 出版日期: 2017-12-26
pages 页数: 238

$34.99


Book Description to Finelybook sorting

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 book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. Assessing a model’s performance is as important as building it; this book will also show you how to do that. Finally, you will see how you can score new data and export your predictions. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models.
Contents
1: INTRODUCTION TO DATA MINING AND PREDICTIVE ANALYTICS
2: THE BASICS OF USING IBM SPSS MODELER
3: IMPORTING DATA INTO MODELER
4: DATA QUALITY AND EXPLORATION
5: CLEANING AND SELECTING DATA
6: COMBINING DATA FILES
7: DERIVING NEW FIELDS
8: LOOKING FOR RELATIONSHIPS BETWEEN FIELDS
9: INTRODUCTION TO MODELING OPTIONS IN IBM SPSS MODELER
10: DECISION TREE MODELS
11: MODEL ASSESSMENT AND SCORING
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
Authors
Jesus Salcedo
Jesus Salcedo has a Ph.D. in Psychometrics from Fordham University. He is an independent statistical and data-mining consultant that has been using SPSS products for over 20 years. He is a former SPSS Curriculum Team Lead and Senior Education Specialist who has written numerous SPSS training courses and trained thousands of users.
Keith McCormick
Keith McCormick is an independent data miner, trainer, conference speaker, and author. He has been using statistics software tools since the early 90s, and has been conducting training since 1997. He has been data mining and using IBM SPSS Modeler since its arrival in North America in the late 90s. He is also an expert in other packages, IBM’s SPSS software suite, including IBM SPSS Statistics, AMOS, and Text Mining. He blogs and reviews related books as well.

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