AI-Driven Predictive Modeling of Marriage Rates in Nigeria: Analyzing the Roles of Tradition, Education, Age and Socioeconomic Status

Ugwuja; Nnenna Esther; Omankwu; Obinnaya Chinecherem Beloved

doi:10.56201/ijcsmt.vol.10.no6.2024.pg169.180

📄 Download Paper

AI-Driven Predictive Modeling of Marriage Rates in Nigeria: Analyzing the Roles of Tradition, Education, Age and Socioeconomic Status

Ugwuja, Nnenna Esther and Omankwu, Obinnaya Chinecherem Beloved

Abstract

This study presents an AI-driven approach to predicting marriage rates in Nigeria by analyzing the influence of socio-cultural and economic factors. Despite the central role marriage plays in Nigerian society, existing literature provides limited insight into how tradition, geopolitical zones, age, physical attractiveness, educational attainment, family background, and employment status collectively affect marital trends. This research applies supervised machine learning techniques— including logistic regression, decision trees, and random forest classifiers—to develop predictive models based on demographic data and survey responses. The analysis reveals that age, educational qualification, and employment status are the most significant predictors of marriage likelihood. Traditional norms and regional affiliations also contribute substantially, though their influence varies across Nigeria’s geopolitical zones. The study underscores the value of artificial intelligence in modeling complex social phenomena and offers a replicable methodological framework for social forecasting in diverse cultural settings. The findings have practical implications for policymakers, sociologists, and religious institutions seeking data-driven insights into marital behavior, and highlight the transformative potential of AI in interdisciplinary social research.

Keywords

Artificial IntelligenceMarriage RateNigeriaSociocultural FactorsPredictive

References

Adebowale, A. S., & Olorunsola, E. O. (2020). Socioeconomic determinants of age at first marriage among women in Nigeria. African Population Studies, 34(1), 4878–4890. https://doi.org/10.11564/34-1-1511 Ajayi, K. & Omolade, B. (2019). Cultural values and marriage stability in Nigeria: An exploration of regional variation. Journal of Sociology and Social Work, 7(2), 58–68. https://doi.org/10.15640/jssw.v7n2a7 Akpan, E. J. (2018). The influence of education and employment on marriage decisions among Nigerian youth. Nigerian Journal of Social Studies, 21(2), 123–140. Chukwuemeka, E. E., & Ugwu, J. O. (2021). Artificial intelligence in social science research: Methodological innovations and challenges in the Nigerian context. International Journal of Computational Social Science, 4(2), 89–104. https://doi.org/10.1016/j.ijcss.2021.06.005 Ekong, E. E., & Nwankwo, D. U. (2018). Family background and its influence on marriage decisions in South-East Nigeria. Journal of Social and Policy Issues, 15(1), 71–85. Isiugo-Abanihe, U. C. (2020). Changing patterns of marriage and family formation in Nigeria. African Population Studies, 34(2), 1–15. Kalu, B. A., & Okonkwo, N. E. (2022). Machine learning techniques for social behavior prediction: A case study on marriage intentions among Nigerian youth. Journal of Applied Artificial Intelligence, 36(7), 602–618. https://doi.org/10.1080/08839514.2022.2033090 National Bureau of Statistics (NBS). (2021). Demographic and Health Survey: Marriage and Family Planning Indicators. Abuja, Nigeria: NBS. Obielodan, O. O., & Adeniran, T. A. (2019). Geopolitical and religious factors influencing early marriage in Northern Nigeria. Journal of Gender and Cultural Studies, 4(1), 34–47. Onyejekwe, C. J., & Eze, U. E. (2017). Big data and artificial intelligence in Nigeria: Opportunities and ethical implications. Nigerian Journal of Technological Research, 12(3), 78–91. Shapley, L. S. (1953). A value for n-person games. Contributions to the Theory of Games, 2(28), 307–317. (Foundational reference for SHAP values) Uddin, M. S., & Zahan, I. (2021). A machine learning approach to predicting social behavior: Modeling marriage intention using logistic regression and random forests. Social Science Computer Review, 39(4), 627–640. https://doi.org/10.1177/0894439320982337 World Bank. (2022). Marriage patterns and fertility rates in Sub-Saharan Africa. Washington, DC: World Bank Publications. https://databank.worldbank.org

AI-Driven Predictive Modeling of Marriage Rates in Nigeria: Analyzing the Roles of Tradition, Education, Age and Socioeconomic Status

Abstract

Keywords

References

More Articles from INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY