Predicting the Income Groups and Number of Immigrants by Using Machine Learning (ML)

Abstract

Migration is one of the biggest problems in the history of mankind. It is important to predict human migration as accurately as possible in terms of many aspects such as urban planning, trade, pandemics, the spread of diseases, and public policy development. With the help of Artificial Intelligence (AI), which is now used in almost all areas of life, it is possible to make predictions about migration. The purpose of this study is to predict the income groups and the number of immigrants by using ML algorithms. Two different applications were carried out in the study. The first one was about predicting the income groups of immigrants and the second one was about predicting the number of immigrants. Data used in the study was obtained from the World Bank. In the first application of the study, Support Vector Machines (SVM), Naive Bayes (NB), Logistic Regression (LR), K-Nearest Neighbors (KNN) were used. In the second application of the study, Random Forest (RF), and Xgboost algorithms were used. As a result of the experiments conducted in the study, 98.37% success rates were obtained with Xgboost, 96.42% with RF, 86.04% with LR, 83.72% with SVM, 83.72% with KNN, and 69.76% with NB. The results of the study reveal that the highest success in the applications was achieved with the LR and Xgboost algorithms. In general, the predictive machine learning models of human migration used in this study will provide a flexible base with which to model human migration under different what-if conditions.

Keywords

• Artificial Intelligence (AI)
• Machine Learning (ML)
• Migration
• Data Science

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