Leveraging Machine Learning to Predict Future Human Development - Gsim, Jamal; Zeriab Es-sadek, Mohamed; Sonatha, Yance (JOIV : International Journal on Informatics Visualization, 2025)
This study utilizes a rich repository of global development data to forecast the Human Development Index, harnessing the World Bank's World Development Indicators (WDI) database and the United Nations Development Program 's extensive human development metrics as primary data sources. Employing R as the driving force, this research unfolds through a meticulously structured four-phase methodology. The initial phase encompasses data pre-processing tasks, including web scraping, merging, cleansing, and transforming datasets. Subsequently, exploratory data analysis is conducted to unravel correlations and regression patterns among variables, culminating in the creation of refined data frames. The crux of this study revolves around machine learning, where two distinct random forest models are crafted: one for regression and another for classification purposes. Additionally, authentic development indicators are used to predict the Human Development Index accurately. Beyond merely deploying machine learning techniques, this research highlights the importance of adopting a multifaceted approach to assess and address global development challenges. This study not only aims to predict the Human Development Index but also lays a foundation for future research endeavors in this domain. It opens up avenues for exploring novel methodologies and datasets to make more precise and comprehensive predictions of human development indices. The findings of this research are poised to make a significant contribution to understanding the dynamics of global development and devising effective strategies for promoting human well-being worldwide.