Introduction

Predictive Analytics is the art and science of using data to make better informed decisions. Predictive analytics helps you uncover hidden patterns and relationships in your data that can help you predict with greater confidence what may happen in the future, and provide you with valuable, actionable insights for your organization.

About This Book

Our goal was to make this complex subject as practical as possible, in a way that appeals to everyone from technical experts to non-technical level business strategists.

The subject is complex because it is not really just one subject. It is the combination of at least a few multifaceted fields: data mining, statistics, and mathematics.

Data mining requires an understanding of machine learning and information retrieval. On top of this, mathematics and statistics must be applied to your business domain; be it marketing, actuary service, fraud, crime, or banking.

Most of the current materials on predictive analytics are pretty difficult to read if you don't already have a background in some of the aforementioned subjects. They are filled with complex mathematical equations and modeling techniques. Or, they are at a high level with specific use cases but with little guidance regarding implementation.  We include both, while trying to keep a wide spectrum of readers engaged.

The focus of this book is developing a roadmap for implementing predictive analytics within your organization. Its intended audience is the larger community of business managers, business analysts, data scientists, and information technology professionals.

Maybe you are a business manager and you have heard the buzz about predictive analytics. Maybe you've been working with data mining and you want to add predictive analytics to your skill set. Maybe you know R or Python, but you're totally new to predictive analytics. If this sounds like you, then this book will be a good fit. Even if you have no experience analyzing data, but want or need to derive greater value from your organization’s data, you can also find something of value in this book.

Foolish Assumptions

Without oversimplifying, we have tried to explain technical concepts in non-technical terms, tackling each topic from the ground up.

Even if you are an experienced practitioner, you should find something new, and at the very least, you will gain validation for what you already know, and guidance for establishing best practices.

We also hope to have contributed a few concepts and ideas for the very first time in a major publication like this. For example we explain how you can apply biologically inspired algorithms to predictive analytics.

We assume that the reader will not be a programmer. The code presented in this book is very brief and easy to follow. Readers of all programming levels will benefit from this book, because it is more about learning the process of predictive analytics rather than learning a programming language.

Icons Used in This Book

The following icons in the margins indicate highlighted material that we think could be of interest to you. Next, we describe the meaning of each icon that is used in this book.

The tips are ideas we would like you to take note of. This is usually practical advice you can apply for that given topic.

This icon is rarely used in this book. We may have used it only once or twice in the entire book. The intent is to save you time by bringing to your attention some common pitfalls that you are better off avoiding.

We have made sincere efforts to steer away from the technical stuff. But when we have no choice we make sure to let you know. So if you don’t care too much about the technical stuff you can easily skip this part and you won’t miss much. If the technical stuff is your thing, then you may find these sections fascinating.

This is something we would like you to take a special note of. This is a concept or idea we think is important for you know and remember. An example of this would be a best practice we think it is noteworthy.

Beyond the Book

A lot of extra content that is not in this book is available at www.dummies.com. Go online to find the following:

• The Cheat Sheet for this book is at

  www.dummies.com/cheatsheet/predictiveanalytics

  Here you’ll find the necessary steps needed to build a predictive analytics model and some cases studies of predictive analytics.

• Updates to this book, if we have any, are also available at

  www.dummies.com/extras/predictiveanalytics

Where to Go from Here

Let’s start making some predictions! You can apply predictive analytics to virtually every business domain. Right now there is explosive growth in predictive analytics’ market, and this is just the beginning. The arena is wide open, and the possibilities are endless.