Business Analytics Using R - A Practical Approach

290 Pages·2017·14.52 MB·English

by  Umesh R. Hodeghatta Dr.

Business Analytics
Using R - A Practical
Approach
—
Dr. Umesh R. Hodeghatta
Umesha Nayak
Business Analytics
Using R - A
Practical Approach
Dr. Umesh R. Hodeghatta
Umesha Nayak
Business Analytics Using R - A Practical Approach
Dr. Umesh R. Hodeghatta Umesha Nayak
Bangalore, Karnataka Bangalore, Karnataka
India India
ISBN-13 (pbk): 978-1-4842-2513-4 ISBN-13 (electronic): 978-1-4842-2514-1
DOI 10.1007/978-1-4842-2514-1
Copyright © 2017 by Dr. Umesh R. Hodeghatta and Umesha Nayak
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Contents at a Glance
About the Authors ����������������������������������������������������������������������������xv
About the Technical Reviewer �������������������������������������������������������xvii
■ Chapter 1: Overview of Business Analytics ������������������������������������1
■ Chapter 2: Introduction to R ���������������������������������������������������������17
■ Chapter 3: R for Data Analysis �����������������������������������������������������37
■ Chapter 4: Introduction to descriptive analytics ��������������������������59
■ Chapter 5: Business Analytics Process and Data Exploration ������91
■ Chapter 6: Supervised Machine Learning—Classification ���������131
■ Chapter 7: Unsupervised Machine Learning �������������������������������161
■ Chapter 8: Simple Linear Regression �����������������������������������������187
■ Chapter 9: Multiple Linear Regression ���������������������������������������207
■ Chapter 10: Logistic Regression �������������������������������������������������233
■ Chapter 11: Big Data Analysis—Introduction and
Future Trends ���������������������������������������������������������������������������������257
References �������������������������������������������������������������������������������������267
Index ����������������������������������������������������������������������������������������������273
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Contents
About the Authors ����������������������������������������������������������������������������xv
About the Technical Reviewer �������������������������������������������������������xvii
■ Chapter 1: Overview of Business Analytics ������������������������������������1
1.1 Objectives of This Book ..................................................................3
1.2 Confusing Terminology ...................................................................4
1.3 Drivers for Business Analytics ........................................................5
1.3.1 Growth of Computer Packages and Applications .........................................6
1.3.2 Feasibility to Consolidate Data from Various Sources ..................................7
1.3.3 Growth of Infinite Storage and Computing Capability ..................................7
1.3.4 Easy-to-Use Programming Tools and Platforms ..........................................7
1.3.5 Survival and Growth in the Highly Competitive World ..................................7
1.3.6 Business Complexity Growing out of Globalization ......................................8
1.4 Applications of Business Analytics .................................................8
1.4.1 Marketing and Sales.....................................................................................8
1.4.2 Human Resources ........................................................................................9
1.4.3 Product Design .............................................................................................9
1.4.4 Service Design .............................................................................................9
1.4.5 Customer Service and Support Areas ...........................................................9
1.5 Skills Required for a Business Analyst .........................................10
1.5.1 Understanding the Business and Business Problems ................................10
1.5.2 Understanding Data Analysis Techniques and Algorithms ..........................10
1.5.3 Having Good Computer Programming Knowledge .....................................11
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1.5.4 Understanding Data Structures and Data Storage/Warehousing
Techniques .................................................................................................11
1.5.5 Knowing Relevant Statistical and Mathematical Concepts .......................11
1.6 Life Cycle of a Business Analytics Project ....................................11
1.7 The Framework for Business Analytics ........................................14
1.8 Summary ......................................................................................15
■ Chapter 2: Introduction to R ���������������������������������������������������������17
2.1 Data Analysis Tools .......................................................................17
2.2 R Installation ................................................................................21
2.2.1 Installing R ..................................................................................................21
2.2.2 Installing RStudio .......................................................................................23
2.2.3 Exploring the RStudio Interface ..................................................................23
2.3 Basics of R Programming .............................................................25
2.3.1 Assigning Values .........................................................................................26
2.3.2 Creating Vectors .........................................................................................27
2.4 R Object Types ..............................................................................27
2.5 Data Structures in R .....................................................................29
2.5.1 Matrices ....................................................................................................30
2.5.2 Arrays .........................................................................................................31
2.5.3 Data Frames ...............................................................................................32
2.5.4 Lists ............................................................................................................34
2.5.5 Factors .......................................................................................................35
2.6 Summary ......................................................................................36
■ Chapter 3: R for Data Analysis �����������������������������������������������������37
3.1 Reading and Writing Data.............................................................37
3.1.1 Reading Data from a Text File .....................................................................38
3.1.2 Reading Data from a Microsoft Excel File ..................................................42
3.1.3 Reading Data from the Web ........................................................................44
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3.2 Using Control Structures in R .......................................................45
3.2.1 if-else .........................................................................................................46
3.2.2 for loops .....................................................................................................46
3.2.3 while loops .................................................................................................47
3.2.4 Looping Functions ......................................................................................48
3.2.5 W riting Your Own Functions in R ................................................................55
3.3 Working with R Packages and Libraries .......................................56
3.4 Summary ......................................................................................58
■ Chapter 4: Introduction to descriptive analytics ��������������������������59
4.1 Descriptive analytics ....................................................................62
4.2 Population and sample .................................................................62
4.3 Statistical parameters of interest .................................................63
4.3.1 Mean...........................................................................................................64
4.3.2 Median ........................................................................................................66
4.3.3 Mode...........................................................................................................68
4.3.4 Range .........................................................................................................68
4.3.5 Quantiles ....................................................................................................69
4.3.6 Standard deviation .....................................................................................70
4.3.7 Variance ......................................................................................................73
4.3.8 “ Summary” command in R .........................................................................73
4.4 Graphical description of the data .................................................74
4.4.1 Plots in R ....................................................................................................74
4.4.2 Histogram ...................................................................................................77
4.4.3 Bar plot .......................................................................................................77
4.4.4 Boxplots ......................................................................................................78
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4.5 Computations on data frames ......................................................79
4.5.1 Scatter plot .................................................................................................81
4.6 Probability ....................................................................................84
4.6.1 Probability of mutually exclusive events ....................................................85
4.6.2 Probability of mutually independent events ...............................................85
4.6.3 Probability of mutually non-exclusive events: ............................................86
4.6.4 Probability distributions..............................................................................86
4.7 Chapter summary.........................................................................88
■ Chapter 5: Business Analytics Process and Data Exploration ������91
5.1 Business Analytics Life Cycle .......................................................91
5.1.1 Phase 1: Understand the Business Problem ...............................................91
5.1.2 Phase 2: Collect and Integrate the Data .....................................................92
5.1.3 Phase 3: Preprocess the Data.....................................................................92
5.1.4 Phase 4: Explore and Visualize the Data .....................................................92
5.1.5 Phase 5: Choose Modeling Techniques and Algorithms .............................93
5.1.6 Phase 6: Evaluate the Model ......................................................................93
5.1.7 Phase 7: Report to Management and Review .............................................94
5.1.8 Phase 8: Deploy the Model .........................................................................94
5.2 Understanding the Business Problem ..........................................94
5.3 Collecting and Integrating the Data ..............................................95
5.3.1 Sampling ....................................................................................................96
5.3.2 Variable Selection .......................................................................................97
5.4 Preprocessing the Data ................................................................97
5.4.1 Data Types ..................................................................................................97
5.4.2 Data Preparation .........................................................................................99
5.4.3 Data Preprocessing with R ......................................................................100
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5.5 Exploring and Visualizing the Data .............................................104
5.5.1 Tables .......................................................................................................105
5.5.2 Summary Tables .......................................................................................106
5.5.3 Graphs ......................................................................................................106
5.5.4 Scatter Plot Matrices ................................................................................112
5.5.5 Data Transformation .................................................................................117
5.6 Using Modeling Techniques and Algorithms ..............................118
5.6.1 Descriptive Analytics ................................................................................118
5.6.2 Predictive Analytics ..................................................................................118
5.6.3 Machine Learning .....................................................................................119
5.7 Evaluating the Model ..................................................................122
5.7.1 T raining Data Partition ..............................................................................122
5.7.2 T est Data Partition ....................................................................................122
5.7.3 V alidation Data Partition ...........................................................................122
5.7.4 Cross-Validation .......................................................................................123
5.7.5 Classification Model Evaluation ................................................................123
5.7.6 Regression Model Evaluation ...................................................................127
5.8 Presenting a Management Report and Review ..........................128
5.8.1 Problem Description ................................................................................128
5.8.2 Data Set Used ...........................................................................................128
5.8.3 Data Cleaning Carried Out ........................................................................128
5.8.4 Method Used to Create the Model ............................................................128
5.8.5 Model Deployment Prerequisites..............................................................128
5.8.6 Model Deployment and Usage ..................................................................129
5.8.7 Issues Handling ........................................................................................129
5.9 Deploying the Model ..................................................................129
5.10 Summary ..................................................................................130
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■ Contents
■ Chapter 6: Supervised Machine Learning—Classification ���������131
6.1 What Is Classification? What Is Prediction? ...............................131
6.2 Probabilistic Models for Classification .......................................132
6.2.1 Example ....................................................................................................133
6.2.2 Naïve Bayes Classifier Using R .................................................................134
6.2.3 Advantages and Limitations of the Naïve Bayes Classifier .......................136
6.3 Decision Trees ............................................................................136
6.3.1 Recursive Partitioning Decision-Tree Algorithm .......................................138
6.3.2 Information Gain .......................................................................................138
6.3.3 Example of a Decision Tree ......................................................................140
6.3.4 Induction of a Decision Tree .....................................................................142
6.3.5 Classification Rules from Tree ..................................................................145
6.3.6 Overfitting and Underfitting ......................................................................145
6.3.7 Bias and Variance .....................................................................................147
6.3.8 Avoiding Overfitting Errors and Setting the Size of Tree Growth ..............148
6.4 Other Classifier Types .................................................................150
6.4.1 K-Nearest Neighbor .................................................................................150
6.4.2 Random Forests .......................................................................................152
6.5 Classification Example Using R ..................................................153
6.6 Summary ....................................................................................160
■ Chapter 7: Unsupervised Machine Learning �������������������������������161
7.1 Clustering - Overview.................................................................161
7.2 What Is Clustering? ....................................................................163
7.2.1 Measures Between Two Records .............................................................163
7.2.2 Distance Measures for Categorical Variables ...........................................164
7.2.3 Distance Measures for Mixed Data Types.................................................165
7.2.4 Distance Between Two Clusters ...............................................................166
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