Application of decision tree model for prediction of immigration policy in different countries of the world

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In the past few decades, the ever-increasing dynamics of international migration flows can be observed. At this stage, the governments of major countries in the world are striving to balance the needs of their citizens and the support of immigrants. The paper analyzes factors that affect the immigration policies of various countries and determines the role of ecological factors (such as environmental conditions). The objective of the study is to predict the immigration policies of different countries of the world based on the analysis of the influencing factors, including environmental performance. The research method is based on the use of the RapidMiner software package to build two decision tree models and a static index database of more than 150 countries around the world. The results show that in most cases, the immigration policies of various countries will focus on maintaining the current level of immigration and increasing the number of skilled workers. At the same time, one of the key decision-making factors will be the country’s current immigration level, environmental performance, GDP per capita, and the Education index. One of the main conclusions is that the country’s environmental indicators have begun to become one of the priority values that determine the state immigration policy. This can be explained by the rising global community interest in the challenges of climate change.

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    • Figure 1. Classification of the main types of immigration policies based on the UN approach
    • Figure 2. General view of 1st and 2nd classification models
    • Figure 3. Decision tree for classification of policy on immigration
    • Figure 4. Decision tree for classification of immigration policy on highly skilled workers
    • Table 1. Key indicators for calculating the model
    • Table A1. Data set for 1st Model “Decision tree for classification of policy on immigration” and 2nd Model “Decision tree for classification of policy on immigration”
    • Conceptualization
      Viktoriia Apalkova, Sergiy Tsyganov, Nataliia Meshko
    • Data curation
      Viktoriia Apalkova, Nadiia Tsyganova
    • Formal Analysis
      Viktoriia Apalkova, Sergiy Tsyganov, Nataliia Meshko, Nadiia Tsyganova
    • Funding acquisition
      Viktoriia Apalkova, Sergiy Tsyganov, Nataliia Meshko, Nadiia Tsyganova, Serhii Apalkov
    • Investigation
      Viktoriia Apalkova, Nadiia Tsyganova
    • Methodology
      Viktoriia Apalkova, Sergiy Tsyganov, Nataliia Meshko
    • Project administration
      Viktoriia Apalkova, Sergiy Tsyganov
    • Resources
      Viktoriia Apalkova, Serhii Apalkov
    • Supervision
      Viktoriia Apalkova, Sergiy Tsyganov, Nataliia Meshko
    • Software
      Viktoriia Apalkova, Serhii Apalkov
    • Validation
      Viktoriia Apalkova, Nadiia Tsyganova
    • Visualization
      Viktoriia Apalkova, Serhii Apalkov
    • Writing – original draft
      Viktoriia Apalkova, Serhii Apalkov
    • Writing – review & editing
      Sergiy Tsyganov