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by Juyoung Song (Author)
About the Author
Dr Juyoung Song is an Assistant Professor of Criminal Justice and Criminology at Pennsylvania State University-Schuylkill, USA. She earned her Bachelors and Masters degrees from the College of Law at Hanyang University, South Korea, and then obtained her doctorate degree in Criminal Justice from Michigan State University, USA. She has published several articles on cyberbullying, underage prostitution, and juvenile delinquency in the International Journal of Offender Therapy, Comparative Criminology, and Journal of Criminal Justice, and has published five books about big data analysis in Korean. Dr Tae Min Song is a Professor in the Department of Health Management at Sahmyook University, South Korea, and is a Visiting Research Fellow at the Big Data Research Center of the Korean Institute for Health and Social Affairs, where he worked as a data scientist and policy maker for 36 years. He holds a PhD in Computer Science, and has authored numerous journal articles in Computers in Human Behavior, Journal of Adolescence Heath, and various other journals. He has also published five books related to big data analysis in Korean.
About this Book
This book provides a detailed description of the entire study process concerning gathering and analysing big data and making observations to develop a crime-prediction model that utilises its findings. It offers an in-depth discussion of several processes, including text mining, which extracts useful information from online documents; opinion mining, which analyses the emotions contained in documents; machine learning for crime prediction; and visualization analysis. To accurately predict crimes using machine learning, it is necessary to procure high-quality training data. Machine learning combined with high-quality data can be used to develop excellent crime-prediction artificial intelligences. As such, the book will serve to be a practical guide to anyone wishing to predict rapidly-changing social phenomena and draw creative conclusions using big-data analysis.
Brief Contents
Installation and Use of R 1
Installation of R 1
Use of R 7
Scientific Research Design 35
Research Concepts 36
Variable Measurement 37
Unit of Analysis 39
Sampling and Hypothesis Testing 39
Statistical Analysis 44
Overview of Machine Learning 118
Introduction 118
Machine Learning Training Data 122
Development of a Cyber bullying Prediction Model Based on Machine Learning 124
Naïve Bayes Classification Model 124
Logistic Regression Model 130
Random Forest Model 134
Decision Tree Model 141
Neural Network Model 149
Support Vector Machine Model 162
Association Analysis 170
Cluster Analysis and Segmentation 179
Machine Learning Model Evaluation 186
Machine Learning Model Evaluation Using Misclassification Tables 189
Machine Learning Model Evaluation Using ROC Curves 208
Artificial Intelligence 215
Calculate the Effect of Input Variables on Output Variables (Prediction Probability) 215
Using Training Data with Input Variables to Create Dependent Variables 221
Creating Data with the Same Training-Data and Predicted-Data Classifications 225
Evaluating Existing Training Data and High Quality Training Data 228
Creating an Artificial Intelligence with Machine Learning 230
Visualization 236
Visualization of Text Data 236
Visualization of Time Series Data 239
Visualization of Geographical Data 250
Developing Machine Learning–Based Predictive Models of Adverse Drug Responses 258
Introduction 258
Research Subjects and Analysis Method 263
Result 269
Discussion and Conclusion 302
Index 307
Pages : 311
ISBN-10 : 1527533883
ISBN-13 : 978-1527533882
Publisher : Cambridge Scholars Publishing; 1st Edition (July 1, 2019)
Language : English
Cambridge__Big Data Analysis Using Machine Learning for Social Scientists and Cr.pdf
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