Organizations: Astronomisches Rechen-Institut der Universität Heidelberg, 69120 Heidelberg, Germany · Fakultüt für Physik und Astronomie der Universität Heidelberg, 69120 Heidelberg, Germany · SBI Capital Markets Limited, Tower-I, World Trade Center, Nauroji Nagar, New Delhi, 110029, India · Institute of Computational Chemistry and Catalysis, University of Girona, Girona, Catalunya, 17004 Spain · Indian Oil Corporation Limited, 3079/3, Sadiq Nagar, J B Tito Marg, New Delhi, 110049, India · Hindalco Industries Limited, Senapati Bapat Marg, Prabhadevi, Mumbai 400013, India
Astrology has long been used to interpret human personality, estimate compatibility, and guide social decision-making. Zodiac-based systems in particular remain culturally influential across much of the world, including in South Asian societies where astrological reasoning can shape marriage matching, naming conventions, ritual timing, and broader life planning. Despite this persistence, astrology has never established either a physically plausible mechanism or a statistically reliable predictive foundation. In this work, we examine zodiac-based personality prediction using a controlled machine-learning framework. We construct a synthetic dataset in which individuals are assigned zodiac signs and personality labels drawn from a shared pool of 100 broadly human traits. Each sign is associated with a subset of 10 common descriptors, intentionally overlapping with those assigned to other signs, thereby reproducing the ambiguity characteristic of practical astrological systems. We then train Logistic Regression, Random Forest, and neural-network classifiers to infer personality labels from zodiac-based features and nuisance covariates. Across all experiments, predictive performance remains at or near random expectation, while shuffled-label controls yield comparable accuracies. We argue that the apparent success of astrology arises not from measurable predictive structure, but from trait universality, category overlap, cognitive biases such as the Barnum effect and confirmation bias, and the interpretive flexibility of astrologers and pundits. We conclude that zodiac-based systems do not provide reliable information for predicting human behavior and instead function as culturally durable narrative frameworks. This paper is intended as a humorous academic exercise.
Figures & tables
Figure 1: The twelve zodiac signs used as categorical input features in this study. The figure illustrates the symbolic partition being tested, not evidence for predictive validity.
Figure 2: Comparison of model performance for real labels, shuffled labels, and a random baseline. In a properly constructed weak-signal setup, the differences remain small and statistically unconvincing.
Figure 3: Confusion matrix for the best-performing model. The absence of strong diagonal dominance indicates that the classifier fails to recover a robust mapping between zodiac sign and personality label.
Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia · Institute of Communication Studies, National Yang Ming Chiao Tung University, Taiwan · Institute of Information Management, National Yang Ming Chiao Tung University, Taiwan +2