Human Migration Analysis Using Machine Learning

Human Migration Analysis Using Machine Learning

Narendra Kumar Rao Bangole, Lingam Thanvitha, T. Benazir Suraiya, Y. N. V. Shashank, N. Loka Harshith
Copyright: © 2024 |Pages: 12
DOI: 10.4018/979-8-3693-3459-1.ch005
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Abstract

When we consider data analysis and machine learning, we usually discover it beneficial for business applications. However, both have immense potential to assist in the resolution of a wide range of issues which are classified as “social phenomena”. The aim of the project is to offer a machine learning solution for a problem that falls under that category: human migration. The project's main goal is to research datasets, preprocess datasets, develop a machine learning model to predict whether a country's net human migration rate (the number of incoming human migrants vs the number of outgoing human migrants) fell into the category of positive or negative. The methodology involves data pre-processing, feature engineering, and the application of machine learning algorithms such as decision trees, neural networks. The model is trained and validated using historical data, ensuring its accuracy and generalizability.
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Importance

Enhanced Resource Allocation: The predictive model for Net Human Migration Rate aids in optimizing resource allocation for countries. Governments and organizations can allocate resources more efficiently by anticipating trends and planning for potential influxes or outflows of migrants.

Proactive Intervention Strategies: The ability to predict a country's Net Human Migration Rate categorically empowers authorities to implement proactive intervention strategies. This ensures timely responses to emerging migration patterns, fostering preparedness and adaptability.

Holistic Social Issue Resolution: Human Migration is a complex social issue with multifaceted implications. The project contributes to a more comprehensive understanding of migration dynamics, addressing not only economic aspects but also sociodemographic and cultural factors that influence human migration trends.

Long-Term Planning: The predictive capabilities of the Machine Learning model enable long-term planning for countries. Governments can devise sustainable policies that consider future demographic shifts, thereby contributing to stability and societal development (Sirkeci et al., 2019).

Cross-Border Collaboration: The project fosters the potential for cross-border collaboration in addressing migration challenges. Shared insights gained through data analysis can facilitate international cooperation in developing effective solutions and mitigating the impact of human migration.

Global Impact: Given the global nature of migration, the project's significance extends beyond individual countries. Insights derived from the analysis can contribute to a broader understanding of migration trends, fostering international collaboration for more effective global policies.

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