COVER
PREFACE
1. DATA SCIENCE USING PYTHON AND R
ABOUT THE AUTHORS
ACKNOWLEDGMENTS
1. CHANTAL'S ACKNOWLEDGMENTS
2. DANIEL'S ACKNOWLEDGMENTS
Chapter 1: INTRODUCTION TO DATA SCIENCE
1. 1.1 WHY DATA SCIENCE?
2. 1.2 WHAT IS DATA SCIENCE?
3. 1.3 THE DATA SCIENCE METHODOLOGY
4. 1.4 DATA SCIENCE TASKS
5. EXERCISES
Chapter 2: THE BASICS OF PYTHON AND R
1. 2.1 DOWNLOADING PYTHON
2. 2.2 BASICS OF CODING IN PYTHON
3. 2.3 DOWNLOADING R AND RSTUDIO
4. 2.4 BASICS OF CODING IN R
5. REFERENCES
6. EXERCISES
Chapter 3: DATA PREPARATION
1. 3.1 THE BANK MARKETING DATA SET
2. 3.2 THE PROBLEM UNDERSTANDING PHASE
3. 3.3 DATA PREPARATION PHASE
4. 3.4 ADDING AN INDEX FIELD
5. 3.5 CHANGING MISLEADING FIELD VALUES
6. 3.6 REEXPRESSION OF CATEGORICAL DATA AS NUMERIC
7. 3.7 STANDARDIZING THE NUMERIC FIELDS
8. 3.8 IDENTIFYING OUTLIERS
9. REFERENCES
10. EXERCISES
Chapter 4: EXPLORATORY DATA ANALYSIS
1. 4.1 EDA VERSUS HT
2. 4.2 BAR GRAPHS WITH RESPONSE OVERLAY
3. 4.3 CONTINGENCY TABLES
4. 4.4 HISTOGRAMS WITH RESPONSE OVERLAY
5. 4.5 BINNING BASED ON PREDICTIVE VALUE
6. REFERENCES
7. EXERCISES
Chapter 5: PREPARING TO MODEL THE DATA
1. 5.1 THE STORY SO FAR
2. 5.2 PARTITIONING THE DATA
3. 5.3 VALIDATING YOUR PARTITION
4. 5.4 BALANCING THE TRAINING DATA SET
5. 5.5 ESTABLISHING BASELINE MODEL PERFORMANCE
6. REFERENCES
7. EXERCISES
Chapter 6: DECISION TREES
1. 6.1 INTRODUCTION TO DECISION TREES
2. 6.2 CLASSIFICATION AND REGRESSION TREES
3. 6.3 THE C5.0 ALGORITHM FOR BUILDING DECISION TREES
4. 6.4 RANDOM FORESTS
5. REFERENCES
6. EXERCISES
Chapter 7: MODEL EVALUATION
1. 7.1 INTRODUCTION TO MODEL EVALUATION
2. 7.2 CLASSIFICATION EVALUATION MEASURES
3. 7.3 SENSITIVITY AND SPECIFICITY
4. 7.4 PRECISION, RECALL, AND F_β SCORES
5. 7.5 METHOD FOR MODEL EVALUATION
6. 7.6 AN APPLICATION OF MODEL EVALUATION
7. 7.7 ACCOUNTING FOR UNEQUAL ERROR COSTS
8. 7.8 COMPARING MODELS WITH AND WITHOUT UNEQUAL ERROR COSTS
9. 7.9 DATA‐DRIVEN ERROR COSTS
10. EXERCISES
Chapter 8: NAÏVE BAYES CLASSIFICATION
1. 8.1 INTRODUCTION TO NAÏVE BAYES
2. 8.2 BAYES THEOREM
3. 8.3 MAXIMUM A POSTERIORI HYPOTHESIS
4. 8.4 CLASS CONDITIONAL INDEPENDENCE
5. 8.5 APPLICATION OF NAÏVE BAYES CLASSIFICATION
6. REFERENCES
7. EXERCISES
Chapter 9: NEURAL NETWORKS
1. 9.1 INTRODUCTION TO NEURAL NETWORKS
2. 9.2 THE NEURAL NETWORK STRUCTURE
3. 9.3 CONNECTION WEIGHTS AND THE COMBINATION FUNCTION
4. 9.4 THE SIGMOID ACTIVATION FUNCTION
5. 9.5 BACKPROPAGATION
6. 9.6 AN APPLICATION OF A NEURAL NETWORK MODEL
7. 9.7 INTERPRETING THE WEIGHTS IN A NEURAL NETWORK MODEL
8. 9.8 HOW TO USE NEURAL NETWORKS IN R
9. REFERENCES
10. EXERCISES
Chapter 10: CLUSTERING
1. 10.1 WHAT IS CLUSTERING?
2. 10.2 INTRODUCTION TO THE k‐MEANS CLUSTERING ALGORITHM
3. 10.3 AN APPLICATION OF k‐MEANS CLUSTERING
4. 10.4 CLUSTER VALIDATION
5. 10.5 HOW TO PERFORM k‐MEANS CLUSTERING USING PYTHON
6. 10.6 HOW TO PERFORM k‐MEANS CLUSTERING USING R
7. EXERCISES
Chapter 11: REGRESSION MODELING
1. 11.1 THE ESTIMATION TASK
2. 11.2 DESCRIPTIVE REGRESSION MODELING
3. 11.3 AN APPLICATION OF MULTIPLE REGRESSION MODELING
4. 11.4 HOW TO PERFORM MULTIPLE REGRESSION MODELING USING PYTHON
5. 11.5 HOW TO PERFORM MULTIPLE REGRESSION MODELING USING R
6. 11.6 MODEL EVALUATION FOR ESTIMATION
7. 11.7 STEPWISE REGRESSION
8. 11.8 BASELINE MODELS FOR REGRESSION
9. REFERENCES
10. EXERCISES
Chapter 12: DIMENSION REDUCTION
1. 12.1 THE NEED FOR DIMENSION REDUCTION
2. 12.2 MULTICOLLINEARITY
3. 12.3 IDENTIFYING MULTICOLLINEARITY USING VARIANCE INFLATION FACTORS
4. 12.4 PRINCIPAL COMPONENTS ANALYSIS
5. 12.5 AN APPLICATION OF PRINCIPAL COMPONENTS ANALYSIS
6. 12.6 HOW MANY COMPONENTS SHOULD WE EXTRACT?
7. 12.7 PERFORMING PCA WITH k = 4
8. 12.8 VALIDATION OF THE PRINCIPAL COMPONENTS
9. 12.9 HOW TO PERFORM PRINCIPAL COMPONENTS ANALYSIS USING PYTHON
10. 12.10 HOW TO PERFORM PRINCIPAL COMPONENTS ANALYSIS USING R
11. 12.11 WHEN IS MULTICOLLINEARITY NOT A PROBLEM?
12. REFERENCES
13. EXERCISES
Chapter 13: GENERALIZED LINEAR MODELS
1. 13.1 AN OVERVIEW OF GENERAL LINEAR MODELS
2. 13.2 LINEAR REGRESSION AS A GENERAL LINEAR MODEL
3. 13.3 LOGISTIC REGRESSION AS A GENERAL LINEAR MODEL
4. 13.4 AN APPLICATION OF LOGISTIC REGRESSION MODELING
5. 13.5 POISSON REGRESSION
6. 13.6 AN APPLICATION OF POISSON REGRESSION MODELING
7. REFERENCE
8. EXERCISES
Chapter 14: ASSOCIATION RULES
1. 14.1 INTRODUCTION TO ASSOCIATION RULES
2. 14.2 A SIMPLE EXAMPLE OF ASSOCIATION RULE MINING
3. 14.3 SUPPORT, CONFIDENCE, AND LIFT
4. 14.4 MINING ASSOCIATION RULES
5. 14.5 CONFIRMING OUR METRICS
6. 14.6 THE CONFIDENCE DIFFERENCE CRITERION
7. 14.7 THE CONFIDENCE QUOTIENT CRITERION
8. REFERENCES
9. EXERCISES
APPENDIX DATA SUMMARIZATION AND VISUALIZATION
1. PART 1: SUMMARIZATION 1: BUILDING BLOCKS OF DATA ANALYSIS
2. PART 2: VISUALIZATION: GRAPHS AND TABLES FOR SUMMARIZING AND ORGANIZING DATA
3. PART 3: SUMMARIZATION 2: MEASURES OF CENTER, VARIABILITY, AND POSITION
4. PART 4: SUMMARIZATION AND VISUALIZATION OF BIVARIATE RELATIONSHIPS
INDEX
END USER LICENSE AGREEMENT

List of Tables

Chapter 3
1. TABLE 3.1 Reexpressing the values of education as numeric
Chapter 7
1. TABLE 7.1 General form of contingency table for binary classifications
2. TABLE 7.2 General form of contingency table, reexpressed
3. TABLE 7.3 Contingency table for evaluating Model 1
4. TABLE 7.4 Evaluation measures of the R C5.0 model
5. TABLE 7.5 Cost matrix for the retailer
6. TABLE 7.6 Adjusted cost matrix for the lender
7. TABLE 7.7 Contingency table for evaluating Model 2, which incorporates asymme...
8. TABLE 7.8 Data‐driven cost matrix for the clothing store problem
9. TABLE 7.9 Simplified data‐driven cost matrix for the clothing store problem....
10. TABLE 7.10 Contingency table for Model 3 with data‐driven error costs
11. TABLE 7.11 Model evaluation metrics for all models
Chapter 8
1. TABLE 8.1 A summary of the Naïve Bayes model for predicting wine type based o...
Chapter 9
1. TABLE 9.1 Data inputs and initial values for neural network weights
2. TABLE 9.2 Contingency table of Sex and Death
3. TABLE 9.3 Contingency table of Sex and Death with column percentages
Chapter 10
1. TABLE 10.1 Mean variable value, by cluster, for the white_wine_training data ...
2. TABLE 10.2 Mean variable value, by cluster, for the white_wine_test data sets...
3. TABLE 10.3 Difference in variable means, training set minus test set, by clus...
Chapter 12
1. TABLE 12.1 Tiny population for our toy example
2. TABLE 12.2 Sample 1 from our tiny population
3. TABLE 12.3 Sample 2 from our tiny population
Chapter 14
1. TABLE 14.1 Transactions made at the roadside vegetable stand
2. TABLE 14.2 Transactions where squash was bought
Appendix
1. Table A.1 Characteristics of 10 loan applicants
2. Table A.2 Frequency distribution and relative frequency distribution
3. Table A.3 Frequency distribution and relative frequency distribution of incom...
4. Table A.4 Cumulative frequency distribution and cumulative relative frequency...
5. Table A.5 Contingency table for Mortgage versus Risk

List of Illustrations

Chapter 1
1. Figure 1.1 Data science methodology: the seven phases.
Chapter 2
1. Figure 2.1 The Spyder window when you first open the program.
2. Figure 2.2 The button and hover text for running a selection or line of Pytho...
3. Figure 2.3 Creating a contingency table in Python without saving the output....
4. Figure 2.4 Creating a contingency table in Python and saving the output under...
5. Figure 2.5 In Python we run the name of the saved output, crosstab_01, to vie...
6. Figure 2.6 Python shows us the variable values of the first record in the ban...
7. Figure 2.7 Python shows the values of the age variable (first four values sho...
8. Figure 2.8 Python shows the values of the age and job variables (first four v...
9. Figure 2.9 An example of a histogram displayed in the output section of Spyde...
10. Figure 2.10 The Preferences window in Spyder as you prepare to change the gra...
11. Figure 2.11 The new window from Spyder that holds our graphical output, with ...
12. Figure 2.12 The RStudio window when you first open the program.
13. Figure 2.13 The button, and hover text, for running a selection or line of R ...
14. Figure 2.14 After clicking the “Import Dataset” button in RStudio, a drop‐dow...
15. Figure 2.15 The “Import Dataset” window in R for the bank_marketing_training ...
16. Figure 2.16 Creating a contingency table in R without saving the output.
17. Figure 2.17 Creating a contingency table in R and saving the output under the...
18. Figure 2.18 The output of the saved table t1 in R.
19. Figure 2.19 R shows us the contents of the first, third, and fourth records i...
20. Figure 2.20 R shows us the contents of the first and third variables (first f...
Chapter 3
1. Figure 3.1 Histogram from R of days_since_previous, with most values near 100...
2. Figure 3.2 Histogram of days_since_previous in Python with missing values pro...
3. Figure 3.3 Histogram in R of days_since_previous, with the missing values exc...
4. Figure 3.4 Bar graph in R of the education variable.
5. Figure 3.5 Histogram in R of education_numeric.
Chapter 4
1. Figure 4.1 Bar graph from R of previous_outcome with response overlay.
2. Figure 4.2 Normalized bar graph from R of previous_outcome with response over...
3. Figure 4.3 Normalized bar graph from Python of previous_outcome with response
4. Figure 4.4 Contingency table from R of previous_outcome with response.
5. Figure 4.5 Contingency table from R of previous_outcome with response table w...
6. Figure 4.6 Histogram from R of age with response overlay.
7. Figure 4.7 Normalized histogram from R of age with response overlay.
8. Figure 4.8 Histogram from Python of age with response overlay.
9. Figure 4.9 Normalized histogram from Python of age with response overlay.
10. Figure 4.10 Bar graph from R of age_binned with response overlay.
11. Figure 4.11 Normalized bar graph from R of age_binned with response overlay. ...
12. Figure 4.12 Contingency table from R of age_binned and response.
13. Figure 4.13 Contingency table from R of age_binned and response with column p...
14. Figure 4.14 Bar graph from Python of age_binned with an overlay of response. ...
15. Figure 4.15 Normalized bar graph from Python of age_binned with an overlay of...
Chapter 5
1. Figure 5.1 The Python table of response values after rebalancing.
2. Figure 5.2 The R table of response values after rebalancing.
Chapter 6
1. Figure 6.1 Decision tree from R for classifying response outcomes for the adu...
2. Figure 6.2 C5.0 decision tree output from R.
Chapter 8
1. Figure 8.1 Contingency table from R for Type and Alcohol_flag.
2. Figure 8.2 Contingency table from R for Type and Sugar_flag.
3. Figure 8.3 Normalized bar graphs of Type with an overlay of Alcohol_flag (on ...
4. Figure 8.4 Contingency table from R of actual wine Type versus predicted wine...
5. Figure 8.5 Contingency tables from Python for Type and Alcohol_flag (left), a...
6. Figure 8.6 Contingency table from Python of actual and predicted wine types f...
7. Figure 8.7 The output from the Naïve Bayes model in R.
Chapter 9
1. Figure 9.1 Real neuron and artificial neuron model.
2. Figure 9.2 Simple example of a neural network.
3. Figure 9.3 Details of neural network, showing input to node A, combination fu...
4. Figure 9.4 Graph of the sigmoid function y = f(x) = 1/(1 + e^–x).
5. Figure 9.5 Histogram from R of Age, with Death overlay.
6. Figure 9.6 Normalized histogram from R of Age, with Death overlay.
7. Figure 9.7 Neural network model from Framingham Heart Study data.
Chapter 10
1. Figure 10.1 Clusters should have small within‐cluster variation compared to t...
2. Figure 10.2 The Python descriptions of Cluster 1 (left) and Cluster 2 (right)...
3. Figure 10.3 The Python descriptions of Cluster 1 (left) and Cluster 2 (right)...
4. Figure 10.4 The R descriptions of Cluster 1 and Cluster 2 from the training d...
5. Figure 10.5 The R descriptions of Cluster 1 (left) and Cluster 2 (right) from...
Chapter 11
1. Figure 11.1 Python regression results for the training data set.
2. Figure 11.2 Python validation of the regression results with the test data se...
3. Figure 11.3 Final regression model for the training data set using Python.
4. Figure 11.4 Validating the final regression model with the test data set usin...
5. Figure 11.5 Regression results from R for the training data set.
6. Figure 11.6 Validating the regression results from R with the test data set. ...
7. Figure 11.7 Final regression results from R for the training data set.
8. Figure 11.8 Validating the final regression results from R with the test data...
9. Figure 11.9 The standard error of the estimate from Python.
10. Figure 11.10 from Python for the final regression model.
11. Figure 11.11 The MAE from Python.
12. Figure 11.12 Standard error and Adjusted R‐squared from R.
13. Figure 11.13 The MAE from R.
14. Figure 11.14 The output from stepwise regression in R.
Chapter 12
1. Figure 12.1 When the predictors x₁ and x₂ are uncorrelated, the response surf...
2. Figure 12.2 Multicollinearity: when the predictors are correlated, the respon...
3. Figure 12.3 Matrix plot of the predictors from Python. Fiber and potassium ar...
4. Figure 12.4 Regression results, with variance inflation factors from R indica...
5. Figure 12.5 Scatterplot matrix of the predictors in R.
6. Figure 12.6 Correlation matrix from R shows substantial correlation among the...
7. Figure 12.7 Regression from R shows some tending toward moderately large VIFs...
8. Figure 12.8 Excerpt from PCA results from R.
9. Figure 12.9 Plot of eigenvalues from R, with dotted line at eigenvalue of 1. ...
10. figure 12.10 (a) component weights with no rotation, from r. (b) component we...
11. Figure 12.11 Proportions of variance explained from R for the test data set. ...
12. Figure 12.12 Component weights from R for the test data set.
13. Figure 12.13 R output showing the principal components are uncorrelated.
14. Figure 12.14 R output showing that regression using the principal components ...
15. Figure 12.15 Correlation matrix from Python.
Chapter 13
1. Figure 13.1 Python logistic regression results for the training data set.
2. Figure 13.2 Python logistic regression results for the test data set.
3. Figure 13.3 Logistic regression results from R for the training data set.
4. Figure 13.4 Logistic regression results from R for the test data set.
5. Figure 13.5 Python Poisson regression results for predicting number of custom...
6. Figure 13.6 Poisson regression results from R.
Chapter 14
1. Figure 14.1 Proportions for International Plan, Voicemail Plan, and Churn fro...
2. Figure 14.2 Proportions for Customer Service Calls from R.
3. Figure 14.3 The first 10 association rules uncovered by R, sorted by lift.
4. Figure 14.4 The top 10 rules from all. rules from R, sorted by lift value.
5. Figure 14.5 Contingency table of Churn and Customer Service Calls from R.
6. Figure 14.6 Association rule found using confidence difference lower bound = ...
7. Figure 14.7 Association rules in R found using confidence quotient lower boun...

This edition first published 2019
© 2019 John Wiley & Sons, Inc.

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Library of Congress Cataloging‐in‐Publication Data

Names: Larose, Chantal D., author. | Larose, Daniel T., author.
Title: Data science using Python and R / Chantal D. Larose, Eastern Connecticut State University, Connecticut, USA, Daniel T. Larose, Central Connecticut State University, Conntecticut, USA.
Description: Hoboken, NJ : John Wiley & Sons, Inc, 2019. | Includes index. |
Identifiers: LCCN 2019007280 (print) | LCCN 2019009632 (ebook) | ISBN 9781119526834 (Adobe PDF) | ISBN 9781119526841 (ePub) | ISBN 9781119526810 (hardback)
Subjects: LCSH: Data mining. | Python (Computer program language) | R (Computer program language) | Big data. | Data structures (Computer science)
Classification: LCC QA76.9.D343 (ebook) | LCC QA76.9.D343 L376 2019 (print) | DDC 006.3/12–dc23
LC record available at https://lccn.loc.gov/2019007280

Cover Design: Wiley
Cover Image: © LumenGraphics/Shutterstock

PREFACE

DATA SCIENCE USING PYTHON AND R

Why this Book is Needed

Reason 1. Data Science is Hot. Really hot. Bloomberg called data scientist “the hottest job in America.”¹ Business Insider called it “The best job in America right now.”² Glassdoor.com rated it the best job in the world in 2018 for the third year in a row.³ The Harvard Business Review called data scientist “The sexiest job in the 21st century.”⁴

Reason 2: Top Two Open‐source Tools. Python and R are the top two open‐source data science tools in the world.⁵ Analysts and coders from around the world work hard to build analytic packages that Python and R users can then apply, free of charge.

Data Science Using Python and R will awaken your expertise in this cutting‐edge field using the most widespread open‐source analytics tools in the world. In Data Science Using Python and R, you will find step‐by‐step hands‐on solutions of real‐world business problems, using state‐of‐the‐art techniques. In short, you will learn data science by doing data science.

Written for Beginners and Non‐Beginners Alike

Data Science Using Python and R is written for the general reader, with no previous analytics or programming experience. We know that the information‐age economy is making many English majors and History majors retool to take advantage of the great demand for data scientists.⁶ This is why we provide the following materials to help those who are new to the field hit the ground running.

An entire chapter dedicated to learning the basics of using Python and R, for beginners. Which platform to use. Which packages to download. Everything you need to get started.
An appendix dedicated to filling in any holes you might have in your introductory data analysis knowledge, called Data Summarization and Visualization.
Step‐by‐step instructions throughout. Every instruction for every action.
Every chapter has Exercises, where you may check your understanding and progress.

Those with analytics or programming experience will enjoy having a one‐stop‐shop for learning how to do data science using both Python and R. Managers, CIOs, CEOs, and CFOs will enjoy being able to communicate better with their data analysts and database analysts. The emphasis in this book on accurately accounting for model costs will help everyone uncover the most profitable nuggets of knowledge from the data, while avoiding the potential pitfalls that may cost your company millions of dollars.

Data Science Using Python and R covers exciting new topics, such as the following:

Random Forests,
General Linear Models, and
Data‐driven error costs to enhance profitability.

Data sets and supplemental materials can be found under the Related Resources section at https://bcs.wiley.com/he-bcs/Books?action=index&itemId=1119526817&bcsId=11765 and https://bcs.wiley.com/he-bcs/Books?action=index&itemId=1119526817&bcsId=11712.

Data Science Using Python and R as a Textbook

Data Science Using Python and R naturally fits the role of textbook for a one‐semester course or two‐semester sequence of courses in introductory and intermediate data science. Faculty instructors will appreciate the exercises at the end of every chapter, totaling over 500 exercises in the book. There are three categories of exercises, from testing basic understanding toward more hands‐on analysis of new and challenging applications.

Clarifying the Concepts. These exercises test the students' basic understanding of the material, to make sure the students have absorbed what they have read.
Working with the Data. These applied exercises ask the student to work in Python and R, following the step‐by‐step instructions that were presented in the chapter.
Hands‐on Analysis. Here is the real meat of the learning process for the students, where they apply their newly found knowledge and skills to uncover patterns and trends in new data sets. Here is where the students' expertise is challenged, in near real‐world conditions. More than half of the exercises in the book consist of Hands‐on Analysis.

The following supporting materials are also available to faculty adopters of the book at no cost.

Full solutions manual, providing not just the answers, but how to arrive at the answers.
Powerpoint presentations of each chapter, so that you may help the students understand the material, rather than just assigning them to read it.

To obtain access to these materials, contact your local Wiley representation and ask them to email the authors confirming that you have adopted the book for your course.

Data Science Using Python and R is appropriate for advanced undergraduate or graduate‐level courses. No previous statistics, computer programming, or database expertise is required. What is required is a desire to learn.

How the Book is Structured

Data Science Using Python and R is structured around the Data Science Methodology.

The Data Science Methodology is a phased, adaptive, iterative, approach to the analysis of data, within a scientific framework.

Problem Understanding Phase. First, clearly enunciate the project objectives. Then, translate these objectives into the formulation of a problem that can be solved using data science.
Data Preparation Phase. Data cleaning/preparation is probably the most labor‐intensive phase of the entire data science process.
- Covered in Chapter 3: Data Preparation.
Exploratory Data Analysis Phase. Gain insights into your data through graphical exploration.
- Covered in Chapter 4: Exploratory Data Analysis.
Setup Phase. Establish baseline model performance. Partition the data. Balance the data, if needed.
- Covered in Chapter 5: Preparing to Model the Data.
Modeling Phase. The core of the data science process. Apply state‐of‐the‐art algorithms to uncover some seriously profitable relationships lying hidden in the data.
- Covered in Chapters 6 and 8–14.
Evaluation Phase. Determine whether your models are any good. Select the best‐performing model from a set of competing models.
- Covered in Chapter 7: Model Evaluation.
Deployment Phase. Interface with management to adapt your models for real‐world deployment.

WILEY SERIES ON METHODS AND APPLICATIONS IN DATA MINING

DATA SCIENCE USING PYTHON AND R

PREFACE

DATA SCIENCE USING PYTHON AND R

Why this Book is Needed

Written for Beginners and Non‐Beginners Alike

Data Science Using Python and R as a Textbook

How the Book is Structured

Notes

ABOUT THE AUTHORS

ACKNOWLEDGMENTS

CHANTAL'S ACKNOWLEDGMENTS

DANIEL'S ACKNOWLEDGMENTS