Work

Prediction of gender power dynamics and political representation in Nigeria using machine learning models

Year: 2025

Type: article

Abstract: This study applies machine learning to investigate gendered power dynamics and women's socio-economic and political engagement in Nigeria (1991-2023, World Bank, UNDP, INEC data). We trained Random Forest, Support Vector Machine (SVM), and Neural Network models with k-fold cross-validation, evaluating performance with R2, RMSE, and MSE. The SVM model demonstrated superior performance (R2 = 0.998). Feature analysis revealed that women's industry participation positively correlates with population share and education, while rural residence diminishes their likelihood of being employers. Additionally, K-means clustering of 2023 voting data uncovered regional variations in women's political representation. This research highlights enduring socio-economic and spatial barriers, demonstrating how AI-based evidence can inform gender-sensitive policies for inclusive representation in Nigeria.

Source: Innovation in Computer and Data Sciences

DOI: 10.64389/icds.2025.01122

Authors: Chinyere P. Okechukwu, Emmanuel Chibuogu Asogwa, Obioma Chukwudi Aguwa, Okechukwu J. Obulezi, Mohamed R. Ezzeldin

Institutions: Lovely Professional University, Nnamdi Azikiwe University, Edge Hill University, Qassim University

Language: English

Topic: Gender Politics and Representation

Subfield: Gender Studies

Field: Social Sciences

Domain: Social Sciences

SDGs: Gender equality, Reduced inequalities

Open Access status: hybrid

OpenAlex ID: W4414107352

FWCI: 5.561

Cites:

Cited by: 3

Related to: 10