Andres Fortino - Data Mining and Predictive Analytics for Business Decisions

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Andres Fortino, PhD

NYU School of Professional Studies

MERCURY LEARNING AND INFORMATION

Dulles, Virginia

Boston, Massachusetts

New Delhi

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A. Fortino. Data Mining and Predictive Analytics for Business Decisions.

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To my wife, Kathleen

Data mining is a recent development in the area of data analysis within the last 20 years. With many recent advances in data science, we now have many more tools and techniques available for data analysts to extract information from data sets. This book aims to assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Data mining is a very sophisticated and organized activity with a well-defined process encoded in the CRISP-DM standard. In this book we develop an understanding of the tools and techniques to assist the individual data analyst, but not necessarily a data science team. This book intends to assist individual data analysts in helping them improve their understanding and skills to answer more sophisticated questions.

Most of the exercises use R and Python, todays most common analysis tools. But rather than focus on coding algorithms with these tools, as is most often the case, we employ interactive interfaces to these tools to perform the analysis. That way, we can focus on the technique and its interpretation rather than developing coding skills. We rely on the Jamovi and the JASP interfaces to the R program and the Orange3 data mining interface to Python. Where appropriate, we introduce additional easy-to-acquire and use tools, such as Voyant, for text analytics, that are available as open source. The techniques covered in this book range from basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics.

We follow the CRISP-DM process throughout, but only as a simple guide to the various steps without necessarily implementing all its procedures. We intend to focus on data analytics, not necessarily the more sophisticated data science approaches. This book is for you if you wish to improve your analytical skills and get practical knowledge of some machine learning approaches. Suppose youre looking for a more profound treatment of many of the techniques presented here, such as their mathematical foundations or more detailed considerations in the use of the algorithms. In that case, you are best served by consulting more advanced texts. This book is not meant to explain the origins or characteristics of each method thoroughly. Instead, at the heart of the book is a series of exercises and real-life case studies, putting each technique or tool to work in different business situations. We leave it for other authors and other texts to present the theoretical and explanatory understanding of the tools. A significant contribution of this book is a curated database of business data files that should provide plenty of practice to acquire skills in each of the techniques presented. The exercises and cases in each chapter are presented with step-by-step explanations to help you acquire skills in their use.