Astrological Prediction of Entrepreneurial Potential Using Machine Learning Classification Techniques

The Indian Knowledge System (IKS) incorporates a vast range of fields in which astrology plays a crucial part in its scientific and artistic heritage. In this paper, the field of Indian astronomy within the broader Indian Knowledge System, in conjunction with contemporary machine learning techniques, is explored. This research investigates the feasibility of predicting entrepreneurial potential through astrological birth chart features using supervised machine learning methods. Data are collected via a structured survey, capturing precise birth date, time, and location alongside self-reported entrepreneurial status. Astrological variables such as planetary positions, zodiac signs, house placements, and interplanetary aspects are derived using Indian astrology. These features form the input space for classification algorithms, including Random Forest and Support Vector Machines, aimed at discriminating between entrepreneurial and non-entrepreneurial participants. Model performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score with cross-validation. The study contributes to behavioral analytics by exploring non-traditional predictive factors rooted in cultural and psychological frameworks. Results clarify the extent to which astrological data, when combined with AI techniques, can serve as meaningful predictors of entrepreneurial traits.