Big Data Analysis Using Machine Learning for Social Scientists and Criminologists

✍ Scribed by Juyoung Song

• Publisher: Cambridge Scholars Publishing
• Year: 2019
• Tongue: English
• Leaves: 316
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Table of Contents
Installation and Use of R
Installation of R
Use of R
Scientific Research Design
Research Concepts
Variable Measurement
Unit of Analysis
Sampling and Hypothesis Testing
Statistical Analysis
Overview of Machine Learning
Introduction
Machine Learning Training Data
Development of a Cyber bullying Prediction Model Based on Machine Learning
Naïve Bayes Classification Model
Logistic Regression Model
Random Forest Model
Decision Tree Model
Neural Network Model
Support Vector Machine Model
Association Analysis
Cluster Analysis and Segmentation
Machine Learning Model Evaluation
Machine Learning Model Evaluation Using Misclassification Tables
Machine Learning Model Evaluation Using ROC Curves
Artificial Intelligence
Calculate the Effect of Input Variables on Output Variables (Prediction Probability)
Using Training Data with Input Variables to Create Dependent Variables
Creating Data with the Same Training-Data and Predicted-Data Classifications
Evaluating Existing Training Data and High Quality Training Data
Creating an Artificial Intelligence with Machine Learning
Visualization
Visualization of Text Data
Visualization of Time Series Data
Visualization of Geographical Data
Developing Machine Learning–Based Predictive Models of Adverse Drug Responses
Introduction
Research Subjects and Analysis Method
Result
Discussion and Conclusion
Index