Reverse migration prediction model based on machine learning / Azreen Anuar

Reverse migration in Malaysia is a relatively new emerging phenomenon where migrants intentionally choose to return to their hometown for better living. Thus, there is a demand to investigate the determinants that lead to these changing population mobility trends in Malaysia. Migration predictions a...

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Main Author:	Anuar, Azreen
Format:	Thesis
Language:	English
Published:	2024
Subjects:	HD Industries Land use Labor
Online Access:	https://ir.uitm.edu.my/id/eprint/107371/1/107371.pdf
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id	my-uitm-ir.107371
record_format	uketd_dc
spelling	my-uitm-ir.1073712024-12-05T07:37:58Z Reverse migration prediction model based on machine learning / Azreen Anuar 2024 Anuar, Azreen HD Industries. Land use. Labor Reverse migration in Malaysia is a relatively new emerging phenomenon where migrants intentionally choose to return to their hometown for better living. Thus, there is a demand to investigate the determinants that lead to these changing population mobility trends in Malaysia. Migration predictions are notorious for bearing high error rate because migrations are the most complicated and unpredictable of the key demographic processes. A significant way to minimize the errors is by using a machine learning approach that can predict reverse migration intelligently depending on the tested dataset. Thus, this research aim to develop a reverse migration prediction model based on machine learning. To fulfil this aim, this research proposed three (3) objectives. The first objective is to identify the factors influencing reverse migration based on the statistics from previous empirical studies through a systematic literature review. The second objective to analyse the relationship among the factors that influence reverse migration in Malaysia using empirical experiments performed through the Shapiro-Wilk and Spearman Correlation analysis. And the third objective is to evaluate reverse migration prediction model based on machine learning analysis. For this purpose, three (3) algorithms have been assessed, namely, the Random Forest, Decision Tree, and Gradient Boosted Tree. The findings of this research have provided new insights into the six (6) factors that could influence reverse migration. In addition, the results from the three (3) algorithms that were tested showed that Random Forest outperforms other algorithms by acquiring an accuracy and classification error to predict reverse migration. With the application of machine learning aligned with Industrial 4.0, this research would be advantageous to predict reverse migration in a more efficient way. 2024 Thesis https://ir.uitm.edu.my/id/eprint/107371/ https://ir.uitm.edu.my/id/eprint/107371/1/107371.pdf text en public masters Universiti Teknologi MARA (UiTM) College of Built Environment Mohd Hussain, Nur Huzeima
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mohd Hussain, Nur Huzeima
topic	HD Industries Land use Labor
spellingShingle	HD Industries Land use Labor Anuar, Azreen Reverse migration prediction model based on machine learning / Azreen Anuar
description	Reverse migration in Malaysia is a relatively new emerging phenomenon where migrants intentionally choose to return to their hometown for better living. Thus, there is a demand to investigate the determinants that lead to these changing population mobility trends in Malaysia. Migration predictions are notorious for bearing high error rate because migrations are the most complicated and unpredictable of the key demographic processes. A significant way to minimize the errors is by using a machine learning approach that can predict reverse migration intelligently depending on the tested dataset. Thus, this research aim to develop a reverse migration prediction model based on machine learning. To fulfil this aim, this research proposed three (3) objectives. The first objective is to identify the factors influencing reverse migration based on the statistics from previous empirical studies through a systematic literature review. The second objective to analyse the relationship among the factors that influence reverse migration in Malaysia using empirical experiments performed through the Shapiro-Wilk and Spearman Correlation analysis. And the third objective is to evaluate reverse migration prediction model based on machine learning analysis. For this purpose, three (3) algorithms have been assessed, namely, the Random Forest, Decision Tree, and Gradient Boosted Tree. The findings of this research have provided new insights into the six (6) factors that could influence reverse migration. In addition, the results from the three (3) algorithms that were tested showed that Random Forest outperforms other algorithms by acquiring an accuracy and classification error to predict reverse migration. With the application of machine learning aligned with Industrial 4.0, this research would be advantageous to predict reverse migration in a more efficient way.
format	Thesis
qualification_level	Master's degree
author	Anuar, Azreen
author_facet	Anuar, Azreen
author_sort	Anuar, Azreen
title	Reverse migration prediction model based on machine learning / Azreen Anuar
title_short	Reverse migration prediction model based on machine learning / Azreen Anuar
title_full	Reverse migration prediction model based on machine learning / Azreen Anuar
title_fullStr	Reverse migration prediction model based on machine learning / Azreen Anuar
title_full_unstemmed	Reverse migration prediction model based on machine learning / Azreen Anuar
title_sort	reverse migration prediction model based on machine learning / azreen anuar
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	College of Built Environment
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/107371/1/107371.pdf
_version_	1818588222232461312

Reverse migration prediction model based on machine learning / Azreen Anuar

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