Machine Learning Projections for Human Development Index Anticipation

Abstract

This study utilizes a rich repository of global development data to forecast the Human Development Index (HDI) by harnessing the World Bank's World Development Indicators (WDI) database and the United Nations Development Program's (UNDP) extensive human development metrics as primary data reservoirs. 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 harnessed to predict the Human Development Index accurately. Beyond merely deploying machine learning techniques, this research underscores the importance of embracing a multifaceted approach to assess and tackle global development challenges. This study not only aims to predict the Human Development Index but also aims to lay a foundation for future research endeavors in this domain. It opens avenues for exploring novel methodologies and datasets for more precise and comprehensive predictions of human development indices. The findings of this research are poised to contribute significantly to understanding the dynamics of global development and devising effective strategies for fostering human well-being worldwide.