Credits Author Copy Editor Cory Lesmeister Manisha Sinha Reviewers Project Coordinator Doug Ortiz Nidhi Joshi Miroslav Kopecky Commissioning Editor Proofreader Veena Pagare Safis Editing Acquisition Editor Indexer Tushar Gupta Mariammal Chettiyar Content Development Editors Graphics Manthan Raja Tania Dutta Jagruti Babaria Technical Editor Production Coordinator Dharmendra Yadav Shraddha Falebhai .
About the Author Cory Lesmeister has over a dozen years of quantitative experience and is currently a Senior Quantitative Manager in the banking industry, responsible for building marketing and regulatory models. Cory spent 16 years at Eli Lilly and Company in sales, market research, Lean Six Sigma, marketing analytics, and new product forecasting. A former U.S. Army active duty and reserve officer, Cory was in Baghdad, Iraq, in 2009 serving as the strategic advisor to the 29,000-person Iraqi Oil Police, where he supplied equipment to help the country secure and protect its oil infrastructure. An aviation aficionado, Cory has a BBA in aviation administration from the University of North Dakota and a commercial helicopter license.
About the Reviewers Doug Ortiz is an Independent Consultant who has been architecting, developing, and integrating enterprise solutions throughout his whole career. Organizations that leverage his skillset have been able to rediscover and reuse their underutilized data via existing and emerging technologies, such as Microsoft BI Stack, Hadoop, NoSQL databases, SharePoint, Hadoop, and related toolsets and technologies.
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Table of Contents Preface 1 Chapter 1: A Process for Success 8 The process 9 Business understanding 10 Identifying the business objective 11 Assessing the situation 12 Determining the analytical goals 12 Producing a project plan 12 Data understanding 13 Data preparation 13 Modeling 14 Evaluation 15 Deployment 15 Algorithm flowchart 16 Summary 21 Chapter 2: Linear Regression - The Blocking and Tackling of Machine Learning 22 Univariate linear regression 23 Business understanding 26 Multivariate linear regression 32 Business understanding 32 Data understanding and preparation 33 Modeling and evaluation 36 Other linear model considerations 50 Qualitative features 50 Interaction terms 52 Summary 54 Chapter 3: Logistic Regression and Discriminant Analysis 55 Classification methods and linear regression 56 Logistic regression 56 Business understanding 57 Data understanding and preparation 58 Modeling and evaluation 63.
Preface "A man deserves a second chance, but keep an eye on him" -John Wayne It is not so often in life that you get a second chance. I remember that only days after we stopped editing the first edition, I kept asking myself, "Why didn't I.?", or "What the heck was I thinking saying it like that?", and on and on. In fact, the first project I started working on after it was published had nothing to do with any of the methods in the first edition. I made a mental note that if given the chance, it would go into a second edition.
1 A Process for Success "If you don't know where you are going, any road will get you there." - Robert Carrol "If you can't describe what you are doing as a process, you don't know what you're doing." - W. Edwards Deming At first glance, this chapter may seem to have nothing to do with machine learning, but it has everything to do with machine learning (specifically, its implementation and making change happen). The smartest people, best software, and best algorithms do not guarantee success, no matter how well it is defined.