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Predictive Analytics For Dummies

ISBN: 978-1-118-72896-3
360 pages
March 2014
Predictive Analytics For Dummies (1118728963) cover image


Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data

Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions.

Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more.

  • Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses
  • Helps readers see how to shepherd predictive analytics projects through their companies
  • Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more
  • Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data
  • Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere

Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.

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Table of Contents

Introduction 1

Part I: Getting Started with Predictive Analytics 5

Chapter 1: Entering the Arena 7

Chapter 2: Predictive Analytics in the Wild 19

Chapter 3: Exploring Your Data Types and Associated Techniques 43

Chapter 4: Complexities of Data 57

Part II: Incorporating Algorithms in Your Models 73

Chapter 5: Applying Models 75

Chapter 6: Identifying Similarities in Data 89

Chapter 7: Predicting the Future Using Data Classification 115

Part III: Developing a Roadmap 145

Chapter 8: Convincing Your Management to Adopt Predictive Analytics 147

Chapter 9: Preparing Data 167

Chapter 10: Building a Predictive Model 177

Chapter 11: Visualization of Analytical Results 189

Part IV: Programming Predictive Analytics 205

Chapter 12: Creating Basic Prediction Examples 207

Chapter 13: Creating Basic Examples of Unsupervised Predictions 233

Chapter 14: Predictive Modeling with R 249

Chapter 15: Avoiding Analysis Traps 275

Chapter 16: Targeting Big Data 295

Part V: The Part of Tens 307

Chapter 17: Ten Reasons to Implement Predictive Analytics 309

Chapter 18: Ten Steps to Build a Predictive Analytic Model 319

Index 331

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Author Information

Dr. Anasse Bari is a Fulbright scholar, a software engineer, and a data mining expert. Mohamed Chaouchi has conducted extensive research using data mining methods in both health and financial domains. Tommy Jung has worked extensively on natural language processing and algorithmic trading using machine learning.

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Download TitleSizeDownload
In the Trenches of Predictive Analytics
Overseeing planning at your organization
63.34 KB Click to Download
Nature-Inspired Predictive Analytics
Valuable tips from a surprising source
76.89 KB Click to Download
Learning Data Mining with R
Getting off the ground with powerful programming
73.80 KB Click to Download
Ten Qualities for Your Predictive Analytics Team
Watch for these skills when you select members
64.81 KB Click to Download
Bonus Chapter
Ten major predictive analytics vendors
69.28 KB Click to Download
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