Data Analysis with Machine Learning for Psychologists: Crash Course to Learn Python 3 and Machine Learning in 10 hours

169 Pages·2022·9.285 MB·English

by  Chandril Ghosh

Chandril Ghosh
Data Analysis
with Machine
Learning
for Psychologists
Crash Course to Learn Python 3 and
Machine Learning in 10 hours
Data Analysis with Machine Learning
for Psychologists
Chandril Ghosh
Data Analysis with Machine
Learning for Psychologists
Crash Course to Learn Python 3 and Machine
Learning in 10 hours
Chandril Ghosh
Department of Psychology
Bath Spa University
Newton Park Campus, Bath, UK
ISBN 978-3-031-14633-6 ISBN 978-3-031-14634-3 (eBook)
https://doi.org/10.1007/978-3-031-14634-3
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
Switzerland AG 2022
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This book is dedicated to the Psychologists
of the future.
Acknowledgement
This book is a collection of ideas from the fascinating field of machine learning. I
am thankful to all those researchers and practitioners of machine learning who con-
ducted the research that I have picked and introduced in this book. Without their
work, this book would not had been possible. There are also several anonymous
authors on websites like Stack Overflow whom I could not cite but their advice,
ideas and codes continue to inspire me and this book. I must acknowledge their
contribution too. I had also been fortunate to receive insights and feedback from my
colleagues, and students. In particular, I would like to thank Mr Richard Joltes, Mr
Varun Thakre and Prof Deepak Padmanabhan for reviewing and providing feedback
on various chapters of the book. I should thank my parents Chandan and Papiya
Ghosh, and my partner Madhumanti Das for their support and faith in me. Finally, I
must value and appreciate the constant support of the Project Coordinator Ms.
Olivia Ramya Chitranjan, (ex) Senior Editor Lilith Dorko and the team of Springer.
They have helped me make this book a reality.
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Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Before Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1 Overview (of Old Ways to Analyse Data and Some
Problems Related to Them) . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.2 Who Am I? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.1.3 How Did I Get There? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.1.4 Who Is This Book For? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.1.5 Who Is This Book NOT For? . . . . . . . . . . . . . . . . . . . . . . . . 8
1.1.6 What’s Special You Get in This Book? . . . . . . . . . . . . . . . . 9
1.1.7 So, What Does This Book Have? . . . . . . . . . . . . . . . . . . . . . 9
1.1.8 How to Best Make Use of This Book? . . . . . . . . . . . . . . . . 9
1.2 Types of Research Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.2.1 Explanatory Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.2.2 Predictive Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.2.3 Exploratory Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.3.1 To Collect or Not Collect Your Own Data . . . . . . . . . . . . . . 17
1.3.2 Where to Get the Data From? . . . . . . . . . . . . . . . . . . . . . . . 19
1.3.3 Ways in Which Data Is Divided . . . . . . . . . . . . . . . . . . . . . . 23
1.3.4 Five Lessons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.4 Statistics: A Refresher Before Getting into Machine Learning . . . . 26
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2 Python Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.1 But Do I Have to Learn to Code for Data Analysis? . . . . . . . . . . . . 34
2.2 How to Install Python? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.3 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.4 Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.4.1 Arithmetic Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.4.2 Comparison Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
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2.5 Statements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.6 Loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.7 Data Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.8 Methods and Functions (Built-Ins) in Python . . . . . . . . . . . . . . . . . 48
2.8.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.8.2 Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
2.9 Error Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.10 Last Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3 Data Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.2 Data Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2.1 Problem 1: Duplicate Columns and Categorical
Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.2.2 Problem 2: Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.2.3 Problem 3: Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.3 Data Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.3.1 Converting Categorical Variables into Numeric
Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.3.2 Converting Continuous Variables into Categorical
Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.3.3 Feature Scaling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.4 Data Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.4.1 Strategy 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.4.2 Strategy 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
3.4.3 Strategy 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
3.4.4 Strategy 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.4.5 Strategy 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
3.5 Final Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.2 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.2.1 Getting Started with Supervised Machine Learning . . . . . . 101
4.2.2 Machine Learning (Classifier): The Leak-Proof
Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
4.2.3 Confidence Interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
4.2.4 Choosing the Best Model for Classification . . . . . . . . . . . . 119
4.2.5 Optimising the Predictive Accuracies of the Model
with Hyperparameter Tuning . . . . . . . . . . . . . . . . . . . . . . . . 123
4.3 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
4.3.1 Regression Using Machine Learning and How
to Interpret the Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
4.3.2 Feature Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
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4.3.3 Exploratory Research Using Unsupervised Machine
Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
4.4 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
4.4.1 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
4.4.2 K-Means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
4.5 Principal Component Analysis (PCA) . . . . . . . . . . . . . . . . . . . . . . . 148
4.6 Rule Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
5 End Note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
About the Author
Chandril Ghosh is a UK-based chartered psychologist and is currently working as

the Lecturer in Clinical/Counselling Psychology at the Bath Spa University. He
completed his BSc in psychology (with honours) and MSc in Clinical Psychology
from India. After completing his MSc, Ghosh began to study machine learning and
Python programming through books and online materials on the subject. He had no
background or prior experience with coding or computer science back then. During
his doctoral studies, he utilised his knowledge on the subject to employ machine
learning techniques to explore psychopathology. You will find his work published
by the BMC Psychiatry journal. Around the same time, he was hired multiple times
to design and deliver a crash course on Python 3 and machine learning for post-
graduate students at Queen’s University Belfast. Furthermore, he continues to run
online courses on the subject outside the University and teaches students from about
56 countries. This book is a product of an accumulation of his hundreds of hours of
teaching and feedback from students with social science backgrounds.
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