Big Data Analysis Using Machine Learning for Social Scientists and Criminologists
1st Edition
Published on 12. July 2019
311 pages
978-1-5275-3679-1 (ISBN)
Description
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 utilizes 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.
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07/2019
Cambridge Scholars Publishing
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Content
- Intro
- 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
