Machine Learning and Astrology: Can Algorithms Predict Human Outcomes Better

Abstract

Predictive modeling has been revolutionized across domains as a result of the rapid growth of machine learning, which has raised doubts regarding its potential to surpass traditional belief-based systems such as astrology in terms of anticipating human outcomes. using astrological frameworks that rely on astronomical configurations and symbolic interpretation, the comparative prediction capabilities of algorithmic models produced inside Machine Learning and astrological frameworks. The purpose of this study is to investigate the ways in which machine learning models make use of massive datasets, statistical patterns, and computational approaches in order to develop predictions concerning behavior, decision-making, and life outcomes. On the other hand, astrology is based on broad correlations between planetary positions and human characteristics, and therefore does not have any empirical validation or consistent repeatability for its predictions. The research shows the methodological contrasts between data-driven prediction and interpretive belief systems by comparing these two techniques and revealing the differences between them. In addition, the research investigates the psychological attractiveness of astrology, despite the fact that machine learning models have been shown to be accurate. There are a number of factors that contribute to the continuous relevance of astrology, even in environments that are technologically advanced. These factors include cognitive biases, emotional demands, and cultural effects.