An Intelligent Astrological Insight Model: A Data-Driven Approach to Profession Prediction Using Hybrid Classifiers

Astrology nowadays is interested in the prediction of professions based on insights gained from astrological guidance. I have endeavored to explore a horoscope-based profession prediction model based on the combination of Randomized Weighted Synthetic Minority Over-sampling Technique and various classifiers like Linear Regression, Binary CART, Naive Bayes, Decision Tree, Stumping, and Hybrid Decision Tree with Naive Bayes Statistics. This mainly addresses class imbalance in the dataset and improves predictability on profession outcomes derived from features such as zodiac signs and planetary positions in a horoscope.