Human Migration Prediction System Using Ensemble Learning

The Human Migration Prediction System utilizes ensemble learning techniques to predict whether an individual will migrate based on various socio-economic and environmental factors. By combining the strengths of decision trees, random forests, AdaBoost, and gradient boosting algorithms, the system provides accurate predictions. This project addresses the pressing need for effective migration prediction tools to aid policymakers, humanitarian organizations, and individuals in making informed decisions.

Keywords: AdaBoost, Gradient Boosting, Migration Forecasting, Predictive Modeling, Accurate Predictions.