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

Get to grips with the fundamentals of data mining and predictive analytics with IBM SPSS Modeler

Key Features

• 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

Book Description

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.

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

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.

Table of Contents

1. Introduction to Data Mining
2. The Basics of Using Modeler
3. Importing Data into Modeler
4. Data Quality and Exploration
5. Cleaning and Selecting Data
6. Combining Data Files
7. Combining and Restructuring Data
8. Looking for Relationships between Fields
9. Introduction to Modeling Options in IBM SPSS Modeler
10. Decision Tree Models
11. Model Assessment and Scoring

掌握IBM SPSS Modeler的數據挖掘和預測分析基礎

主要特點:
- 不需深入了解，即可快速上手使用IBM SPSS Modeler。
- 辨識數據中有趣的關聯性，並建立有效的數據挖掘和預測分析解決方案。
- 由業界最佳人士撰寫的快速、易於理解的指南，讓您對SPSS Modeler有基本的理解。

書籍描述:
IBM SPSS Modeler允許用戶快速高效地使用預測分析，從數據中獲取洞察力。作為歷史悠久的數據挖掘工作台，Modeler是最成熟和全面的工具。由於在企業環境中廣泛使用，在大學環境中廣泛提供，並且與所有最新技術高度兼容，它是開始您的數據科學和機器學習之旅的完美方式。

本書以詳細的、逐步的方式介紹使用事實上的標準流程CRISP-DM進行數據挖掘，並使用Modeler易於學習的“可視化編程”風格。您將學習如何將數據讀入Modeler，評估數據質量，為建模準備數據，尋找數據中有趣的模式和關聯性，以及導出預測結果。本書通過一個案例研究，有意地簡短而專注地介紹了基本內容。作者們根據數千名新用戶的教學經驗，選擇了您應該首先學習的Modeler方面，以便您能夠從一開始就掌握經過驗證的最佳實踐。

本書概述了各種流行的數據建模技術，並詳細介紹了如何使用CHAID（決策樹模型）。評估模型的性能與構建模型同樣重要，本書還將向您展示如何進行評估。最後，您將了解如何對新數據進行評分並導出預測結果。通過閱讀本書，您將對數據挖掘的基礎知識有牢固的理解，並學會如何有效地使用Modeler建立預測模型。

您將學到什麼:
- 理解數據挖掘的基礎知識，熟悉Modeler的可視化編程界面。
- 將數據導入Modeler，學習如何正確聲明元數據。
- 獲取摘要統計信息，審核數據質量。
- 通過選擇和排序案例、識別和刪除重複數據、合併數據文件以及修改和創建字段，為建模準備數據。
- 使用各種統計和圖形技術評估簡單的關聯性。
- 瞭解Modeler中不同類型的模型。
- 構建決策樹模型並評估其結果。
- 對新數據進行評分並導出預測結果。

本書適合對SPSS Modeler新手，希望能夠快速開始使用，而不需深入細節。對基本數據挖掘概念的理解將有助於更好地利用本書。

目錄:
1. 數據挖掘簡介
2. 使用Modeler的基礎知識
3. 將數據導入Modeler
4. 數據質量和探索
5. 數據清理和選擇
6. 數據文件合併
7. 數據合併和重組
8. 查找字段之間的關聯性
9. IBM SPSS Modeler中建模選項簡介
10. 決策樹模型
11. 模型評估和評分