Prediction of Extra-marital Affairs using Deep Learning Techniques

Mohite, Rohan Sunil (2024) Prediction of Extra-marital Affairs using Deep Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Extramarital affairs usually has a huge effect on the involved parties and hence predicting the occurrences would be helpful in counseling. Extramarital affairs in a marital relationship poses severe emotion and social effects, leading to relational deterioration and high cases of divorce. It is therefore important to anticipate such incidences in order to design practical counseling and intercessions. This research tries to fill this gap by using deep learning approach to predict extra marital affair using demographic background, personality traits and socio-economic status. Two predictive approaches were employed: usually distinguished into binary classification and multiclass classification. Some of the pre-processing steps taken were feature scaling, detection of outlier and feature encoding in particular the categorical variables were encoded using one-hot encoding. Among the models used, there are LSTM networks, CNN, Random Forest and SVM. Evaluation parameters were accuracy, precision, recall, as well as F1 score. The LSTM model performed for binary classification passing with an accuracy of 91.8%, while the CNN model achieved 80.6% for multiclass classification on the same data. These results reveal the potential of deep learning methods for the effective prediction of extramarital affairs and useful knowledge to be applied to relationship counseling and advancement of preventive measures to optimise relationship outcomes.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rustam, Furqan UNSPECIFIED
Subjects:	B Philosophy. Psychology. Religion > Psychology H Social Sciences > HQ The family. Marriage. Woman Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	20 Aug 2025 10:39
Last Modified:	20 Aug 2025 10:39
URI:	https://norma.ncirl.ie/id/eprint/8590

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