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Produktbild: Data Science Using Python and R

Data Science Using Python and R

129,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

09.04.2019

Verlag

John Wiley & Sons

Seitenzahl

256

Maße (L/B/H)

23,5/15,7/1,9 cm

Gewicht

531 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-52681-0

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

09.04.2019

Verlag

John Wiley & Sons

Seitenzahl

256

Maße (L/B/H)

23,5/15,7/1,9 cm

Gewicht

531 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-52681-0

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Data Science Using Python and R

  • Preface xi

    About the Authors xv

    Acknowledgements xvii

    Chapter 1 Introduction to Data Science 1

    1.1 Why Data Science? 1

    1.2 What is Data Science? 1

    1.3 The Data Science Methodology 2

    1.4 Data Science Tasks 5

    1.4.1 Description 6

    1.4.2 Estimation 6

    1.4.3 Classification 6

    1.4.4 Clustering 7

    1.4.5 Prediction 7

    1.4.6 Association 7

    Exercises 8

    Chapter 2 The Basics of Python and R 9

    2.1 Downloading Python 9

    2.2 Basics of Coding in Python 9

    2.2.1 Using Comments in Python 9

    2.2.2 Executing Commands in Python 10

    2.2.3 Importing Packages in Python 11

    2.2.4 Getting Data into Python 12

    2.2.5 Saving Output in Python 13

    2.2.6 Accessing Records and Variables in Python 14

    2.2.7 Setting Up Graphics in Python 15

    2.3 Downloading R and RStudio 17

    2.4 Basics of Coding in R 19

    2.4.1 Using Comments in R 19

    2.4.2 Executing Commands in R 20

    2.4.3 Importing Packages in R 20

    2.4.4 Getting Data into R 21

    2.4.5 Saving Output in R 23

    2.4.6 Accessing Records and Variables in R 24

    References 26

    Exercises 26

    Chapter 3 Data Preparation 29

    3.1 The Bank Marketing Data Set 29

    3.2 The Problem Understanding Phase 29

    3.2.1 Clearly Enunciate the Project Objectives 29

    3.2.2 Translate These Objectives into a Data Science Problem 30

    3.3 Data Preparation Phase 31

    3.4 Adding an Index Field 31

    3.4.1 How to Add an Index Field Using Python 31

    3.4.2 How to Add an Index Field Using R 32

    3.5 Changing Misleading Field Values 33

    3.5.1 How to Change Misleading Field Values Using Python 34

    3.5.2 How to Change Misleading Field Values Using R 34

    3.6 Reexpression of Categorical Data as Numeric 36

    3.6.1 How to Reexpress Categorical Field Values Using Python 36

    3.6.2 How to Reexpress Categorical Field Values Using R 38

    3.7 Standardizing the Numeric Fields 39

    3.7.1 How to Standardize Numeric Fields Using Python 40

    3.7.2 How to Standardize Numeric Fields Using R 40

    3.8 Identifying Outliers 40

    3.8.1 How to Identify Outliers Using Python 41

    3.8.2 How to Identify Outliers Using R 42

    References 43

    Exercises 44

    Chapter 4 Exploratory Data Analysis 47

    4.1 EDA Versus HT 47

    4.2 Bar Graphs with Response Overlay 47

    4.2.1 How to Construct a Bar Graph with Overlay Using Python 49

    4.2.2 How to Construct a Bar Graph with Overlay Using R 50

    4.3 Contingency Tables 51

    4.3.1 How to Construct Contingency Tables Using Python 52

    4.3.2 How to Construct Contingency Tables Using R 53

    4.4 Histograms with Response Overlay 53

    4.4.1 How to Construct Histograms with Overlay Using Python 55

    4.4.2 How to Construct Histograms with Overlay Using R 58

    4.5 Binning Based on Predictive Value 58

    4.5.1 How to Perform Binning Based on Predictive Value Using Python 59

    4.5.2 How to Perform Binning Based on Predictive Value Using R 62

    References 63

    Exercises 63

    Chapter 5 Preparing to Model the Data 69

    5.1 The Story So Far 69

    5.2 Partitioning the Data 69

    5.2.1 How to Partition the Data in Python 70

    5.2.2 How to Partition the Data in R 71

    5.3 Validating your Partition 72

    5.4 Balancing the Training Data Set 73

    5.4.1 How to Balance the Training Data Set in Python 74

    5.4.2 How to Balance the Training Data Set in R 75

    5.5 Establishing Baseline Model Performance 77

    References 78

    Exercises 78

    Chapter 6 Decision Trees 81

    6.1 Introduction to Decision Trees 81

    6.2 Classification and Regression Trees 83

    6.2.1 How to Build CART Decision Trees Using Python 84

    6.2.2 How to Build CART Decision Trees Using R 86

    6.3 The C5.0 Algorithm for Building Decision Trees 88

    6.3.1 How to Build C5.0 Decision Trees Using Python 89

    6.3.2 How to Build C5.0 Decision Trees Using R 90

    6.4 Random Forests 91

    6.4.1 How to Build Random Forests in Python 92

    6.4.2 How to Build Random Forests in R 92

    References 93

    Exercises 93

    Chapter 7 Model Evaluation 97

    7.1 Introduction to Model Evaluation 97

    7.2 Classification Evaluation Measures 97

    7.3 Sensitivity and Specificity 99

    7.4 Precision, Recall, and Fß Scores 99

    7.5 Method for Model Evaluation 100

    7.6 An Application of Model Evaluation 100

    7.6.1 How to Perform Model Evaluation Using R 103

    7.7 Accounting for Unequal Error Costs 104

    7.7.1 Accounting for Unequal Error Costs Using R 105

    7.8 Comparing Models with and without Unequal Error Costs 106

    7.9 DatäDriven Error Costs 107

    Exercises 109

    Chapter 8 Naïve Bayes Classification 113

    8.1 Introduction to Naive Bayes 113

    8.2 Bayes Theorem 113

    8.3 Maximum a Posteriori Hypothesis 114

    8.4 Class Conditional Independence 114

    8.5 Application of Naive Bayes Classification 115

    8.5.1 Naive Bayes in Python 121

    8.5.2 Naive Bayes in R 123

    References 125

    Exercises 126

    Chapter 9 Neural Networks 129

    9.1 Introduction to Neural Networks 129

    9.2 The Neural Network Structure 129

    9.3 Connection Weights and the Combination Function 131

    9.4 The Sigmoid Activation Function 133

    9.5 Backpropagation 134

    9.6 An Application of a Neural Network Model 134

    9.7 Interpreting the Weights in a Neural Network Model 136

    9.8 How to Use Neural Networks in R 137

    References 138

    Exercises 138

    Chapter 10 Clustering 141

    10.1 What is Clustering? 141

    10.2 Introduction to the K¿Means Clustering Algorithm 142

    10.3 An Application of K¿Means Clustering 143

    10.4 Cluster Validation 144

    10.5 How to Perform K¿Means Clustering Using Python 145

    10.6 How to Perform K¿Means Clustering Using R 147

    Exercises 149

    Chapter 11 Regression Modeling 151

    11.1 The Estimation Task 151

    11.2 Descriptive Regression Modeling 151

    11.3 An Application of Multiple Regression Modeling 152

    11.4 How to Perform Multiple Regression Modeling Using Python 154

    11.5 How to Perform Multiple Regression Modeling Using R 156

    11.6 Model Evaluation for Estimation 157

    11.6.1 How to Perform Estimation Model Evaluation Using Python 159

    11.6.2 How to Perform Estimation Model Evaluation Using R 160

    11.7 Stepwise Regression 161

    11.7.1 How to Perform Stepwise Regression Using R 162

    11.8 Baseline Models for Regression 162

    References 163

    Exercises 164

    Chapter 12 Dimension Reduction 167

    12.1 The Need for Dimension Reduction 167

    12.2 Multicollinearity 168

    12.3 Identifying Multicollinearity Using Variance Inflation Factors 171

    12.3.1 How to Identify Multicollinearity Using Python 172

    12.3.2 How to Identify Multicollinearity in R 173

    12.4 Principal Components Analysis 175

    12.5 An Application of Principal Components Analysis 175

    12.6 How Many Components Should We Extract? 176

    12.6.1 The Eigenvalue Criterion 176

    12.6.2 The Proportion of Variance Explained Criterion 177

    12.7 Performing Pca with K = 4 178

    12.8 Validation of the Principal Components 178

    12.9 How to Perform Principal Components Analysis Using Python 179

    12.10 How to Perform Principal Components Analysis Using R 181

    12.11 When is Multicollinearity Not a Problem? 183

    References 184

    Exercises 184

    Chapter 13 Generalized Linear Models 187

    13.1 An Overview of General Linear Models 187

    13.2 Linear Regression as a General Linear Model 188

    13.3 Logistic Regression as a General Linear Model 188

    13.4 An Application of Logistic Regression Modeling 189

    13.4.1 How to Perform Logistic Regression Using Python 190

    13.4.2 How to Perform Logistic Regression Using R 191

    13.5 Poisson Regression 192

    13.6 An Application of Poisson Regression Modeling 192

    13.6.1 How to Perform Poisson Regression Using Python 193

    13.6.2 How to Perform Poisson Regression Using R 194

    Reference 195

    Exercises 195

    Chapter 14 Association Rules 199

    14.1 Introduction to Association Rules 199

    14.2 A Simple Example of Association Rule Mining 200

    14.3 Support, Confidence, and Lift 200

    14.4 Mining Association Rules 202

    14.4.1 How to Mine Association Rules Using R 203

    14.5 Confirming Our Metrics 207

    14.6 The Confidence Difference Criterion 208

    14.6.1 How to Apply the Confidence Difference Criterion Using R 208

    14.7 The Confidence Quotient Criterion 209

    14.7.1 How to Apply the Confidence Quotient Criterion Using R 210

    References 211

    Exercises 211

    Appendix Data Summarization and Visualization 215

    Part 1: Summarization 1: Building Blocks of Data Analysis 215

    Part 2: Visualization: Graphs and Tables for Summarizing and Organizing Data 217

    Part 3: Summarization 2: Measures of Center, Variability, and Position 222

    Part 4: Summarization and Visualization of Bivariate Elationships 225

    Index 231