Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/4616
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dc.contributor.authorAdeliyi, Timothyen_US
dc.contributor.authorOyewusi, Lawrenceen_US
dc.contributor.authorEpizitone, Ayogebohen_US
dc.contributor.authorOyewusi, Damilolaen_US
dc.date.accessioned2023-02-10T07:09:24Z-
dc.date.available2023-02-10T07:09:24Z-
dc.date.issued2022-12-30-
dc.identifier.citationAdeliyi, T. et al. 2022. Analysing factors influencing women unemployment using a random forest model. Hong Kong Journal of Social Sciences. 60 (Autumn/Winter 2022): 382-393.en_US
dc.identifier.issn1021-3619-
dc.identifier.urihttps://hdl.handle.net/10321/4616-
dc.description.abstractThe unemployment crisis has been a persistent issue for both development countries, resulting in an economic indicator deficit. Women are at a disadvantage and continue to encounter significant obstacles to gaining employment. Nigeria, like many other developing countries with high unemployment rates, has a 33% unemployment rate. Consequently, there has been minimal research on the factors that affect women's unemployment. As a result, the purpose of this study investigates the factors women's unemployment in Nigeria. Although the Random Forest model has been widely applied to classification issues, there is a gap in the literature's use of the random forest as a predictor for analyzing factors influencing women's unemployment. The random forest model was employed in this study because of its characteristics such as strong learning ability, robustness, and feasibility of the hypothesis space. As a result, the Random forest prediction model was benchmarked with seven different cutting-edge classical machine learning prediction models, which include the J48 pruned tree, Support Vector Machine, AdaBoost, Logistic Regression, Naive Bayes, Logistic Model Tree, Bagging and Random Forest. The experimental results demonstrate that Random Forest outperformed the other seven machine learning classifier models using ten commonly used performance evaluation metrics. According to the study's findings, age groups, ethnicity, marital status, and religion were the essential factors affecting women's unemployment in Nigeria.en_US
dc.format.extent12 pen_US
dc.language.isoenen_US
dc.relation.ispartofHong Kong Journal of Social Sciences; Vol. 60, Issue Autumn/Winter 2022en_US
dc.subjectMachine learningen_US
dc.subjectNational Demographic and Health Surveyen_US
dc.subjectRandom foresten_US
dc.subjectInfluencing factorsen_US
dc.subjectWomen unemploymenten_US
dc.titleAnalysing factors influencing women unemployment using a random forest modelen_US
dc.typeArticleen_US
dc.date.updated2023-01-18T13:09:56Z-
local.sdgSDG08-
local.sdgSDG05-
local.sdgSDG14-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextWith Fulltext-
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