Divorce Predictor

Research on divorce prediction aims to identify factors contributing to marital dissolution, leveraging machine learning algorithms like support vector machines and naive Bayes to analyze diverse datasets, including court proceedings. Current efforts focus on improving model accuracy and interpretability, using techniques like LIME to understand the factors driving predictions, and addressing biases in data and algorithms that might skew results, particularly concerning gender inequality. This research has implications for improving judicial efficiency through automated dispute detection and offers insights into societal factors influencing divorce rates and the experiences of individuals within the legal system.