Analytics on the Influence of Corruption on Poverty in Developing African Countries

• Tools & Technologies:: Python, Pandas, Scikit-Learn, Random Forest, SVR, Decision Tree, Linear Regression
• Role:: Data Analyst
• Github URL: Project Link

Description:

Performed an advanced statistical and machine-learning investigation into macroeconomic relationships between corruption indicators and poverty severity across African developing nations. Integrated multi-source datasets from the World Bank and Transparency International, managed missingness and scale inconsistencies, and engineered socioeconomic features to capture structural inequality patterns. Modeled poverty outcomes using regression ensembles and assessed feature contributions through permutation importance and sensitivity analysis.

Outcome

Identified a strong positive correlation between corruption prevalence and poverty intensification. Findings highlighted governance weaknesses as statistically significant predictors of economic stagnation and informed policy-oriented discussions on institutional reforms.