  Divorce is one of the most common social issues in developed countries like
in the United States. Almost 50% of the recent marriages turn into an
involuntary divorce or separation. While it is evident that people vary to a
different extent, and even over time, an incident like Divorce does not
interrupt the individual's daily activities; still, Divorce has a severe effect
on the individual's mental health, and personal life. Within the scope of this
research, the divorce prediction was carried out by evaluating a dataset named
by the 'divorce predictor dataset' to correctly classify between married and
Divorce people using six different machine learning algorithms- Logistic
Regression (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 that
algorithms such as SVM, KNN, and LDA, can perform that task with an accuracy of
98.57%. This work's additional novel contribution is the detailed and
comprehensive explanation of prediction probabilities using Local Interpretable
Model-Agnostic Explanations (LIME). Utilizing LIME to analyze test results
illustrates the possibility of differentiating between divorced and married
couples. Finally, we have developed a divorce predictor app considering ten
most important features that potentially affect couples in making decisions in
their divorce, such tools can be used by any one in order to identify their
relationship condition.
