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Divorce is one of the most common social issues in developed countries likein the United States. Almost 50% of the recent marriages turn into aninvoluntary divorce or separation. While it is evident that people vary to adifferent extent, and even over time, an incident like Divorce does notinterrupt the individual's daily activities; still, Divorce has a severe effecton the individual's mental health, and personal life. Within the scope of thisresearch, the divorce prediction was carried out by evaluating a dataset namedby the 'divorce predictor dataset' to correctly classify between married andDivorce people using six different machine learning algorithms- LogisticRegression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN),Classification and Regression Trees (CART), Gaussian Na\"ive Bayes (NB), and,Support Vector Machines (SVM). Preliminary computational results show thatalgorithms such as SVM, KNN, and LDA, can perform that task with an accuracy of98.57%. This work's additional novel contribution is the detailed andcomprehensive explanation of prediction probabilities using Local InterpretableModel-Agnostic Explanations (LIME). Utilizing LIME to analyze test resultsillustrates the possibility of differentiating between divorced and marriedcouples. Finally, we have developed a divorce predictor app considering tenmost important features that potentially affect couples in making decisions intheir divorce, such tools can be used by any one in order to identify theirrelationship condition.

Divorce Prediction with Machine Learning: Insights and LIME Interpretability