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The correct bibliographic citation for this manual is as follows: Gearhart, James. 2020. End-to-End Data Science with

SAS®: A Hands-On Programming Guide. Cary, NC: SAS Institute Inc. End-to-End Data Science with SAS®: A Hands-On Programming Guide

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20 20 , SAS Institute Inc., Cary, NC, USA ISBN 978-1-64295-808-9 (Hard cover) ISBN 978-1-64295-804-1 (Paperback)

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Contents

About This Book ................................................................................................................. vii

About The Author .............................................................................................................. xiii

Chapter 1: Data Science Overview ....................................................................................... 1

Introduction to This Book .........................................................................................................................................1

The Current Data Science Landscape ....................................................................................................................3

Introduction to Data Science Concepts ...................................................................................................................7

Chapter Review ..................................................................................................................................................... 36 Chapter 2: Example Step-by-Step Data Science Project .................................................... 37 Overview ............................................................................................................................................................... 37

Business Opportunity ............................................................................................................................................ 38

Initial Questions ..................................................................................................................................................... 40

Get the Data .......................................................................................................................................................... 43

Select a Performance Measure

............................................................................................................................. 46

Train / Test Split .................................................................................................................................................... 46

Target Variable Analysis ....................................................................................................................................... 48

Predictor Variable Analysis ................................................................................................................................... 54

Adjusting the TEST Data Set ................................................................................................................................ 69

Building a Predictive Model ................................................................................................................................... 70

Decision Time

....................................................................................................................................................... 93

Implementation ...................................................................................................................................................... 96

Chapter Review ................................................................................................................................

97 Chapter 3: SAS Coding ....................................................................................................... 99

Overview ............................................................................................................................................................... 99

Get Data .............................................................................................................................................................. 100

Explore Data ....................................................................................................................................................... 106

Manipulate Data

.................................................................................................................................................. 116

Export Data ......................................................................................................................................................... 126

Chap

ter Review ................................................................................................................................................... 134 Chapter 4: Advanced SAS Coding ..................................................................................... 135

Overview ................................................................................................

135

DO Loop .............................................................................................................................................................. 135

ARRAY Statements ............................................................................................................................................. 137

SCAN Function ................................................................................................................................................... 139 FIND Function ..................................................................................................................................................... 140

iv End-to-End Data Science with SAS

PUT Function ..................................................................................................................................................... 141

FIRST. and LAST. Statements ........................................................................................................................... 142

Macros Overview ................................................................................................................................................ 143

Macro Variables ................................................................................................................................................. 144

Macros................................................................................................................................................................ 147

Defining and Calling Macros ............................................................................................................................... 148

Chapter Review .................................................................................................................................................. 150

Chapter 5: Create a Modeling Data Set ............................................................................ 151

Overview ............................................................................................................................................................ 151

ETL ..................................................................................................................................................................... 152

Extract ................................................................................................................................................................ 152

Data Set ............................................................................................................................................................. 155

Transform ........................................................................................................................................................... 162

Load ................................................................................................................................................................... 187

Chapter Review .................................................................................................................................................. 188

Chapter 6: Linear Regression Models .............................................................................. 189

Overview ............................................................................................................................................................ 189

Regression Structure .......................................................................................................................................... 190

Gradient Descent ............................................................................................................................................... 194

Linear Regression Assumptions ......................................................................................................................... 198

Linear Regression .............................................................................................................................................. 205

Simple Linear Regression .................................................................................................................................. 215

Multiple Linear Regression ................................................................................................................................. 222

Regularization Models ........................................................................................................................................ 228

Chapter Review .................................................................................................................................................. 235

Chapter 7: Parametric Classification Models ................................................................... 237

Overview ............................................................................................................................................................ 237

Classification Overview ...................................................................................................................................... 238

Logistic Regression ............................................................................................................................................ 240

Visualization

....................................................................................................................................................... 244

Logistic Regression Model ................................................................................................................................. 246

Linear Discriminant Analysis .............................................................................................................................. 262

Chapter Review .................................................................................................................................................. 269

Chapter 8: Non

-Parametric Models .................................................................................. 271

Overview ............................................................................................................................................................ 271

Modeling Data Set .............................................................................................................................................. 272

K

Nearest Neighbor Model ................................................................................................................................. 275

Tree-Based Models ............................................................................................................................................ 284

Random Forest ................................................................................................................................................... 299

Gradient Boosting ............................................................................................................................................... 310

Support Vector Machine (SVM) .......................................................................................................................... 316

Contents v

Neural Networks .................................................................................................................................................. 324

Chapter Review ................................................................................................................................................... 331

Chapter 9: Model Evaluation Metrics ............................................................................... 333

Overview ............................................................................................................................................................. 333

General Information............................................................................................................................................. 333

Model Output ....................................................................................................................................................... 337

Accuracy Statistics .............................................................................................................................................. 338

Black-Box Evaluation Tools ................................................................................................................................. 351

Chapter Review ................................................................................................................................................... 358

Index ................................................................................................................................. 359

vi End-to-End Data Science with SAS

About This Book

What Does

This Book Cover?

Hello, my name is James, and I"m an addict. I"m addicted to data science books, web courses, instructional videos, blogs, data science podcasts, predictive modeling competitions, and coding. This addiction takes up the majority of my mental energy. From the time that I wake up until I fall asleep (and all through my dreams), I"m generally thinking about data science concepts and coding. I"m going to bet that many of you are in a similar situation. If so, I"m sure that you have been as frustrated as I have been about the massive hole in the instructional data science market. The market is overrun with data science books for Python, R, and Hadoop. These books provide an overview of data science and in-depth instructions on the various machine learning models, and they provide the associated development code for those particular programming languages. Although these books are great resources for data scientists, they do not offer direct programming instruction to the most popular programming language in the business community. SAS is used by 95% of Fortune 100 companies, and these companies are the leading employers of data scientists. There is an incredible opportunity to fill the need of professional data scientists for hands-on machine learning training with real-world examples. The unfortunate reality for many SAS programmers is that we often do not have access to the latest and greatest SAS products. SAS Enterprise Miner, SAS Visual Analytics, SAS Forecast Server, and SAS Viya are all incredible products, but they are not universally available to all SAS programmers. It is essential that a data scientist who is working in a SAS environment be able to develop and implement machine learning models in any SAS environment. Even if data scientists have access to SAS Viya, it is incredibly beneficial for them to have a solid understanding of the programming code that drives the models that they develop in

SAS Viya.

This book,

End -to-End Data Science in SAS®, provides all SAS programmers insight into the models, methodology, and SAS coding required to develop machine learning models in any industry . It also serves as a reference for programmers of any language who either want to expand their knowledge base or who have just been hired into a data scientist position where

SAS is the preferred language.

viii End-to-End Data Science with SAS The goal of this book is to provide clear and practical explanations of the data science environment, machine learning techniques, and the SAS code necessary for the properquotesdbs_dbs3.pdfusesText_6

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