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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. 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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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