A Machine Learning Approach to Understanding Migration Decisions and Psychological Stress Among Youth

Abstract

In this paper, we considered migration induced predictions by machine learning algorithms. In the determinants of migration, which are age, occupation, psychological pressure and social media effect, the costs are known. The models are trained on a full data set prior to launching. It applies Random Forest, K-Nearest Neighbours (KNN), Support Vector Machines (SVM), XGBoost and Logistic Regression methods to migration tendency prediction. The Random Forest model (best performing) also made high accurate predictions, 81.35% correct predictions also. It also contains a recommendation module for personalized feedback to subjects. Last but not the least, `model interpretability methods’ like LIME and SHAP are used to serve the prediction in an interpretable way. So from that standpoint it’s an experiment for the first time and it may work out. The research ethical considerations involved, such as data privacy, fairness and interpretability, could be said for the opposite case which occurs as the integrity for machine learning can be strengthened. Offering us predictive analytics and implementable solutions to these challenges, it is improving our migration process and emerging as more one governed by public decisions. It offers scalable, interpretable predictions for migration and new directions of research in this domain.