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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

About the Author

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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