Big data analysis using machine learning for social scientists and criminologists / by Juyoung Song, T'ae-min Song.

Format:

Book

Author/Creator:

Song, Juyoung, author.

Song, T'ae-min, author.

Language:

English

Subjects (All):

Data mining--Industrial applications.

Data mining.

Genre:

Libros electrónicos.

Physical Description:

1 online resource (316 pages)

Edition:

1st ed.

Place of Publication:

Newcastle upon Tyne, England : Cambridge Scholars Publishing, 2019.

Summary:

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.

Contents:

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

Research Subjects and Analysis Method

Result

Discussion and Conclusion

Index.

Notes:

Description based on print version record.

ISBN:

1-5275-3679-3

OCLC:

1149140094