Machine Learning for Decision Sciences with Case Studies in Python

477 Pages·2022·18.042 MB·English

by  S. Sumathi, Surekha Paneerselvam, Suresh V. Rajappa, L. Ashok Kumar

Machine Learning for Decision
Sciences with Case Studies
in Python

Machine Learning for Decision
Sciences with Case Studies
in Python
S. Sumathi
Suresh V. Rajappa
L. Ashok Kumar
Surekha Paneerselvam
First edition published 2022
by CRC Press
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© 2022 S. Sumathi, Suresh V. Rajappa, L Ashok Kumar and Surekha Paneerselvam
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ISBN: 978-1-032-19356-4 (hbk)
ISBN: 978-1-032-19357-1 (pbk)
ISBN: 978-1-003-25880-3 (ebk)
DOI: 10.1201/9781003258803
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Contents
Preface..............................................................................................................................................xv
Acknowledgment ...........................................................................................................................xvii
About the Authors ...........................................................................................................................xix
Introduction .....................................................................................................................................xxi
Chapter 1 Introduction ..................................................................................................................1
1.1 Introduction to Data Science .............................................................................1
1.1.1 Mathematics .........................................................................................1
1.1.2 Statistics ...............................................................................................1
1.2 Describing Structural Patterns ..........................................................................2
1.2.1 Uses of Structural Patterns ...................................................................2
1.3 Machine Learning and Statistics .......................................................................3
1.4 Relation between Artificial Intelligence, Machine Learning, Neural
Networks, and Deep Learning ..........................................................................4
1.5 Data Science Life Cycle ....................................................................................6
1.6 Key Role of Data Scientist .................................................................................8
1.6.1 Difference between Data Scientist and Machine
Learning Engineer................................................................................8
1.7 Real-World Examples ........................................................................................8
1.8 Use Cases ...........................................................................................................9
1.8.1 Financial and Insurance Industries ......................................................9
1.8.1.1 Fraud Mitigation ...................................................................9
1.8.1.2 Personalized Pricing ...........................................................10
1.8.1.3 AML – Anti-Money Laundering ........................................10
1.8.2 Utility Industries ................................................................................11
1.8.2.1 Smart Meter and Smart Grid ..............................................11
1.8.2.2 Manage disaster and Outages .............................................11
1.8.2.3 Compliance .........................................................................11
1.8.3 Oil and Gas Industries ........................................................................11
1.8.3.1 Manage Exponential Growth ..............................................11
1.8.3.2 3D Seismic Imaging and Kirchhoff....................................12
1.8.3.3 Rapidly Process and Display Seismic Data ........................12
1.8.4 E-Commerce and Hi-Tech Industries .................................................12
1.8.4.1 Association and Complementary Products .........................12
1.8.4.2 Cross-Channel Analytics ....................................................12
1.8.4.3 Event analytics ....................................................................13
Summary ....................................................................................................................13
Review Questions .......................................................................................................14
Chapter 2 Overview of Python for Machine Learning ...............................................................15
2.1 Introduction .....................................................................................................15
2.1.1 The Flow of Program Execution in Python .......................................15
2.2 Python for Machine Learning .........................................................................15
2.2.1 Why Is Python Good for ML? ............................................................16
2.3 Setting up Python ............................................................................................16
v
vi Contents
2.3.1 Python on Windows ...........................................................................16
2.3.2 Python on Linux .................................................................................17
2.3.2.1 Ubuntu ................................................................................17
2.4 Python Basics ..................................................................................................17
2.4.1 Python Operators ...............................................................................18
2.4.1.1 Arithmetic Operators ..........................................................18
2.4.1.2 Comparison Operators ........................................................18
2.4.1.3 Assignment Operators ........................................................18
2.4.1.4 Logical Operators ...............................................................18
2.4.1.5 Membership Operators .......................................................19
2.4.2 Python Code Samples on Basic Operators .........................................19
2.4.2.1 Arithmetic Operators ..........................................................19
2.4.2.2 Comparison Operators ........................................................21
2.4.2.3 Logical Operators ...............................................................22
2.4.2.4 Membership Operators .......................................................23
2.4.3 Flow Control .......................................................................................24
2.4.3.1 If & elif Statement ..............................................................24
2.4.3.2 Loop Statement ...................................................................25
2.4.3.3 Loop Control Statements ....................................................26
2.4.4 Python Code Samples on Flow Control Statements...........................26
2.4.4.1 Conditional Statements .......................................................26
2.4.4.2 Python if...else Statement....................................................27
2.4.4.3 Python if…elif…else Statement .........................................28
2.4.4.4 The For Loop ......................................................................29
2.4.4.5 The range() Function ..........................................................29
2.4.4.6 For Loop with else ..............................................................31
2.4.4.7 While Loop .........................................................................31
2.4.4.8 While Loop with else..........................................................32
2.4.4.9 Python Break and Continue ................................................32
2.4.4.10 Python Break Statement .....................................................32
2.4.4.11 Python Continue Statement ................................................33
2.4.5 Review of Basic Data Structures and Implementation in Python ......34
2.4.5.1 Array Data Structure ..........................................................34
2.4.5.2 Implementation of Arrays in Python ..................................35
2.4.5.3 Linked List ..........................................................................36
2.4.5.4 Implementation of Linked List in Python ..........................36
2.4.5.5 Stacks and Queues ..............................................................38
2.4.5.6 Queues ................................................................................40
2.4.5.7 Implementation of Queue in Python ...................................41
2.4.5.8 Searching ............................................................................42
2.4.5.9 Implementation of Searching in Python .............................44
2.4.5.10 Sorting ................................................................................46
2.4.5.11 Implementation of Bubble Sort in Python ..........................47
2.4.5.12 Insertion Sort ......................................................................47
2.4.5.13 Implementation of Insertion Sort in Python .......................49
2.4.5.14 Selection Sort ......................................................................51
2.4.5.15 Implementation of Selection Sort in Python.......................52
2.4.5.16 Merge Sort ..........................................................................52
2.4.5.17 Implementation of Merge Sort in Python ...........................53
2.4.5.18 Shell Sort ............................................................................54
2.4.5.19 Quicksort ............................................................................55
Contents vii
2.4.5.20 Data Structures in Python with Sample Codes ..................55
2.4.5.21 Python Code Samples for Data Structures in Python ........58
2.4.6 Functions in Python ...........................................................................68
2.4.6.1 Python Code Samples for Functions ..................................68
2.4.6.2 Returning Values from Functions .......................................68
2.4.6.3 Scope of Variables ..............................................................69
2.4.6.4 Function Arguments ...........................................................70
2.4.7 File Handling ......................................................................................74
2.4.8 Exception Handling ............................................................................74
2.4.9 Debugging in Python .........................................................................75
2.4.9.1 Packages..............................................................................75
2.5 Numpy Basics ..................................................................................................75
2.5.1 Introduction to Numpy .......................................................................76
2.5.1.1 Array Creation ....................................................................76
2.5.1.2 Array Slicing.......................................................................77
2.5.2 Numerical Operations ........................................................................77
2.5.3 Python Code Samples for Numpy Package ........................................78
2.5.3.1 Array Creation ....................................................................78
2.5.3.2 Class and Attributes of ndarray—.ndim .............................82
2.5.3.3 Class and Attributes of ndarray—.shape ............................82
2.5.3.4 Class and Attributes of ndarray—ndarray.size,
ndarray.Itemsize, ndarray.resize .........................................83
2.5.3.5 Class and Attributes of ndarray—.dtype ............................83
2.5.3.6 Basic Operations .................................................................84
2.5.3.7 Accessing Array Elements: Indexing .................................85
2.5.3.8 Shape Manipulation ............................................................88
2.5.3.9 Universal Functions (ufunc) in Numpy ...............................90
2.5.3.10 Broadcasting .......................................................................90
2.5.3.11 Args and Kwargs ................................................................91
2.6 Matplotlib Basics .............................................................................................92
2.6.1 Creating Graphs with Matplotlib .......................................................93
2.7 Pandas Basics ..................................................................................................94
2.7.1 Getting Started with Pandas...............................................................94
2.7.2 Data Frames .......................................................................................95
2.7.3 Key Operations on Data Frames ........................................................95
2.7.3.1 Data Frame from List .........................................................95
2.7.3.2 Rows and Columns in Data Frame .....................................96
2.8 Computational Complexity ..............................................................................97
2.9 Real-world Examples .......................................................................................97
2.9.1 Implementation using Pandas .............................................................98
2.9.2 Implementation using Numpy ............................................................98
2.9.3 Implementation using Matplotlib .......................................................98
Summary ....................................................................................................................99
Review Questions .....................................................................................................100
Exercises for Practice ...............................................................................................101
Chapter 3 Data Analytics Life Cycle for Machine Learning....................................................103
3.1 Introduction ...................................................................................................103
3.2 Data Analytics Life Cycle .............................................................................104
3.2.1 Phase 1 – Data Discovery .................................................................104
viii Contents
3.2.2 Phase 2 – Data Preparation and Exploratory Data Analysis ............107
3.2.2.1 Exploratory Data Analysis ................................................110
3.2.3 Phase 3 – Model Planning ................................................................136
3.2.4 Phase 4 – Model Building ................................................................139
3.2.5 Phase 5 – Communicating Results ...................................................140
3.2.6 Phase 6 – Optimize and Operationalize the Models ........................140
Summary ..................................................................................................................142
Review Questions .....................................................................................................143
Chapter 4 Unsupervised Learning ............................................................................................145
4.1 Introduction ...................................................................................................145
4.2 Unsupervised Learning .................................................................................145
4.2.1 Clustering .........................................................................................147
4.3 Evaluation Metrics for Clustering .................................................................147
4.3.1 Distance Measures ...........................................................................148
4.3.1.1 Minkowski Metric ............................................................149
4.3.2 Similarity Measures .........................................................................149
4.4 Clustering Algorithms ...................................................................................150
4.4.1 Hierarchical and Partitional Clustering Approaches .......................150
4.4.2 Agglomerative and Divisive Clustering Approaches .......................150
4.4.3 Hard and Fuzzy Clustering Approaches ..........................................150
4.4.4 Monothetic and Polythetic Clustering Approaches ..........................151
4.4.5 Deterministic and Probabilistic Clustering Approaches ..................151
4.5 k-Means Clustering ........................................................................................151
4.5.1 Geometric Intuition, Centroids ........................................................151
4.5.2 The Algorithm ..................................................................................152
4.5.3 Choosing k ........................................................................................152
4.5.4 Space and Time Complexity ............................................................153
4.5.5 Advantages and Disadvantages of k-Means Clustering ...................153
4.5.5.1 Advantages ........................................................................153
4.5.5.2 Disadvantages ...................................................................153
4.5.6 k-Means Clustering in Practice Using Python .................................154
4.5.6.1 Illustration of the k-Means Algorithm Using Python .......154
4.5.7 Fuzzy k-Means Clustering Algorithm ..............................................157
4.5.7.1 The Algorithm ..................................................................158
4.5.8 Advantages and Disadvantages of Fuzzy k-Means Clustering ........158
4.6 Hierarchical Clustering .................................................................................159
4.6.1 Agglomerative Hierarchical Clustering ...........................................159
4.6.2 Divisive Hierarchical Clustering ......................................................161
4.6.3 Techniques to Merge Cluster ............................................................161
4.6.4 Space and Time Complexity ............................................................163
4.6.5 Limitations of Hierarchical Clustering ............................................163
4.6.6 Hierarchical Clustering in Practice Using Python ...........................163
4.6.6.1 DATA_SET.......................................................................164
4.7 Mixture of Gaussian Clustering ....................................................................165
4.7.1 Expectation Maximization ...............................................................166
4.7.2 Mixture of Gaussian Clustering in Practice Using Python ..............168
4.8 Density-Based Clustering Algorithm ............................................................169
4.8.1 DBSCAN (Density-Based Spatial Clustering of
Applications with Noise) ..................................................................169
Contents ix
4.8.2 Space and Time Complexity ............................................................171
4.8.3 Advantages and Disadvantages of DBSCAN ...................................171
4.8.3.1 Advantages ........................................................................171
4.8.3.2 Disadvantages ...................................................................171
4.8.4 DBSCAN in Practice Using Python .................................................172
Summary ..................................................................................................................174
Review Questions .....................................................................................................174
Chapter 5 Supervised Learning: Regression ............................................................................177
5.1 Introduction ...................................................................................................177
5.2 Supervised Learning – Real-Life Scenario ...................................................177
5.3 Types of Supervised Learning .......................................................................178
5.3.1 Supervised Learning – Classification ..............................................178
5.3.1.1 Classification – Predictive Modeling ................................179
5.3.2 Supervised Learning – Regression...................................................179
5.3.2.1 Regression Predictive Modeling .......................................180
5.3.3 Classification vs. Regression ............................................................180
5.3.4 Conversion between Classification and Regression Problems .........181
5.4 Linear Regression ..........................................................................................181
5.4.1 Types of Linear Regression ..............................................................182
5.4.1.1 Simple Linear Regression .................................................183
5.4.1.2 Multiple Linear Regression ..............................................184
5.4.2 Geometric Intuition ..........................................................................186
5.4.3 Mathematical Formulation ...............................................................187
5.4.4 Solving Optimization Problem .........................................................201
5.4.4.1 Maxima and Minima ........................................................201
5.4.4.2 Gradient Descent ..............................................................202
5.4.4.3 LMS (Least Mean Square) Update Rule ..........................205
5.4.4.4 SGD Algorithm .................................................................205
5.4.5 Real-World Applications ..................................................................206
5.4.5.1 Predictive Analysis ...........................................................206
5.4.5.2 Medical Outcome Prediction ............................................208
5.4.5.3 Wind Speed Prediction .....................................................208
5.4.5.4 Environmental Effects Monitoring ...................................209
5.4.6 Linear Regression in Practice Using Python ...................................209
5.4.6.1 Simple Linear Regression Using Python ..........................209
5.4.6.2 Multiple Linear Regression Using Python .......................212
Summary ..................................................................................................................215
Review Questions .....................................................................................................215
Chapter 6 Supervised Learning: Classification ........................................................................219
6.1 Introduction ...................................................................................................219
6.2 Use Cases of Classification ............................................................................219
6.3 Logistic Regression .......................................................................................219
6.3.1 Geometric Intuition ..........................................................................220
6.3.2 Variants of Logistic Regression .......................................................222
6.3.2.1 Simple Logistic Regression ..............................................222
6.3.2.2 Multiple Logistic Regression ............................................223
6.3.2.3 Binary Logistic Regression ..............................................223