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introduction to statistical and machine learning methods for data science carlos andre reis pinheiro mike patetta the correct bibliographic citation for this manual is as follows pinheiro carlos andre reis ...

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  Introduction to
  Statistical and 
  Machine Learning 
  Methods for 
  Data Science
          Carlos Andre Reis Pinheiro
                Mike Patetta
      The correct bibliographic citation for this manual is as follows: Pinheiro, Carlos Andre Reis and Mike Patetta. 2021. 
      Introduction to Statistical and Machine Learning Methods for Data Science. Cary, NC: SAS Institute Inc.
      Introduction to Statistical and Machine Learning Methods for Data Science
      Copyright © 2021, SAS Institute Inc., Cary, NC, USA
      ISBN 978-1-953329-64-6 (Hardcover) 
      ISBN 978-1-953329-60-8 (Paperback) 
      ISBN 978-1-953329-61-5 (Web PDF) 
      ISBN 978-1-953329-62-2 (EPUB) 
      ISBN 978-1-953329-63-9 (Kindle)
      All Rights Reserved. Produced in the United States of America. 
      For a hard copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, 
      in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written 
      permission of the publisher, SAS Institute Inc.
      For a web download or e-book: Your use of this publication shall be governed by the terms established by the 
      vendor at the time you acquire this publication.
      The scanning, uploading, and distribution of this book via the Internet or any other means without the permission 
      of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not 
      participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated.
      U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer 
      software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. 
      Use, duplication, or disclosure of the Software by the United States Government is subject to the license terms 
      of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a), and DFAR 
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      SAS Institute Inc., SAS Campus Drive, Cary, NC 27513-2414
      August 2021
      SAS® and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS 
      Institute Inc. in the USA and other countries. ® indicates USA registration.
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        Contents
        About This Book ............................................................................................................................... vii
        About These Authors ......................................................................................................................... ix
        Acknowledgments  .......................................................................................................................... xiii
        Foreword .......................................................................................................................................... xv
        Chapter 1: Introduction to Data Science..............................................................................................1
         Chapter Overview .................................................................................................................................. 1
         Data Science .......................................................................................................................................... 1
          Mathematics and Statistics ............................................................................................................... 3
          Computer Science ............................................................................................................................. 3
          Domain Knowledge ........................................................................................................................... 4
          Communication and Visualization ....................................................................................................5
          Hard and Soft Skills ........................................................................................................................... 6
         Data Science Applications ...................................................................................................................... 6
         Data Science Lifecycle and the Maturity Framework.............................................................................7
          Understand the Question ................................................................................................................. 7
          Collect the Data ................................................................................................................................ 8
          Explore the Data ............................................................................................................................... 9
          Model the Data ................................................................................................................................. 9
          Provide an Answer .......................................................................................................................... 11
         Advanced Analytics in Data Science ....................................................................................................12
         Data Science Practical Examples ..........................................................................................................16
          Customer Experience ...................................................................................................................... 16
          Revenue Optimization .................................................................................................................... 16
          Network Analytics ........................................................................................................................... 17
          Data Monetization .......................................................................................................................... 17
         Summary  ............................................................................................................................................. 18
         Additional Reading .............................................................................................................................. 18
        Chapter 2: Data Exploration and Preparation ....................................................................................19
         Chapter Overview  ............................................................................................................................... 19
         Introduction to Data Exploration   .......................................................................................................20
          Nonlinearity .................................................................................................................................... 20
          High Cardinality ............................................................................................................................... 20
               iv   Introduction to Statistical and Machine Learning Methods for Data Science
                      Unstructured Data .......................................................................................................................... 21
                      Sparse Data ..................................................................................................................................... 21
                      Outliers ........................................................................................................................................... 21
                      Mis-scaled Input Variables ..............................................................................................................21
                   Introduction to Data Preparation ........................................................................................................22
                      Representative Sampling ................................................................................................................22
                      Event-based Sampling ..................................................................................................................... 23
                      Partitioning ..................................................................................................................................... 24
                      Imputation ...................................................................................................................................... 25
                      Replacement ................................................................................................................................... 27
                      Transformation................................................................................................................................ 27
                      Feature Extraction ........................................................................................................................... 29
                      Feature Selection ............................................................................................................................ 32
                   Model Selection ................................................................................................................................... 33
                      Model Generalization ..................................................................................................................... 33
                      Bias–Variance Tradeoff ................................................................................................................... 35
                   Summary  ............................................................................................................................................. 35
               Chapter 3: Supervised Models – Statistical Approach .........................................................................37
                   Chapter Overview ................................................................................................................................ 37
                   Classification and Estimation ...............................................................................................................37
                   Linear Regression ................................................................................................................................. 40
                      Use Case: Customer Value ..............................................................................................................42
                   Logistic Regression ............................................................................................................................... 42
                      Use Case: Collecting Predictive Model ............................................................................................44
                   Decision Tree ....................................................................................................................................... 45
                      Use Case: Subscription Fraud ..........................................................................................................47
                   Summary ............................................................................................................................................. 49
               Chapter 4: Supervised Models – Machine Learning Approach ...........................................................51
                   Chapter Overview  ............................................................................................................................... 51
                   Supervised Machine Learning Models .................................................................................................51
                   Ensemble of Trees................................................................................................................................ 52
                      Random Forest ................................................................................................................................ 52
                      Gradient Boosting ........................................................................................................................... 54
                      Use Case: Usage Fraud .................................................................................................................... 55
                   Neural Network ................................................................................................................................... 56
                      Use Case: Bad Debt ......................................................................................................................... 59
                   Summary  ............................................................................................................................................. 61
               Chapter 5: Advanced Topics in Supervised Models ............................................................................63
                   Chapter Overview ................................................................................................................................ 63
                   Advanced Machine Learning Models and Methods ............................................................................63
                   Support Vector Machines .................................................................................................................... 64
                      Use Case: Fraud in Prepaid Subscribers ..........................................................................................67
                   Factorization Machines ........................................................................................................................ 68
                      Use Case: Recommender Systems Based on Customer Ratings in Retail ........................................70
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...Introduction to statistical and machine learning methods for data science carlos andre reis pinheiro mike patetta the correct bibliographic citation this manual is as follows cary nc sas institute inc copyright usa isbn hardcover paperback web pdf epub kindle all rights reserved produced in united states of america a hard copy book no part publication may be reproduced stored retrieval system or transmitted any form by means electronic mechanical photocopying otherwise without prior written permission publisher download e your use shall governed terms established vendor at time you acquire scanning uploading distribution via internet other illegal punishable law please purchase only authorized editions do not participate encourage piracy copyrighted materials support others appreciated u s government license restricted software its documentation commercial computer developed private expense provided with duplication disclosure subject agreement pursuant applicable far dfar extent requi...

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