Predicting Divorce: Can Algorithms Spot Trouble?

Divorce is a common issue in many countries, affecting nearly half of all marriages. It's not just a legal process; it has a deep impact on mental health and daily life. Researchers used a dataset called the 'divorce predictor dataset' to create a tool that can tell apart married and divorced people. They tried out six different machine learning methods: Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors, Classification and Regression Trees, Gaussian Naive Bayes, and Support Vector Machines.

Some of these, like SVM, KNN, and LDA, did a fantastic job with 98. 57% accuracy. But the researchers didn't stop there. They also used a tool called LIME to explain why the algorithms made certain predictions. This helped to understand what factors might cause a couple to divorce. Finally, they made a simple app that uses the ten most important factors to help people check the health of their relationship.

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