Final project on "Data Insights into the Influence of Corruption on Poverty in Developing Countries in Africa"
#ABSTRACT In today's world, understanding the complex interplay between poverty and corruption is critical for informed policy-making and sustainable development. This research project focuses on selected African countries, aiming to synergize poverty and corruption datasets for insights into the influence of corruption on poverty. Covering hypothesis formulation, meticulous data selection, exploratory analysis, and the application of sophisticated models, this project contributes significantly to understanding the complex relationship between corruption and poverty rates in Africa. The hypothesis, "Do countries with high levels of corruption experience higher poverty rates?" guides the research, emphasizing the urgency of addressing corruption as a fundamental driver of elevated poverty levels. The datasets, sourced from credible repositories, were rigorously cleaned and merged. Employing four models—Linear Regression, Random Forest, Support Vector Regression, and Decision Tree—the analysis sought to discern correlations between key features. Random Forest emerged as the most robust performer, with Support Vector Regression as a close contender. In conclusion, this research offers valuable empirical insights into the interplay between corruption and poverty in African countries. By addressing corruption as a pivotal factor in perpetuating high poverty rates, the findings advocate for implementing targeted anti-corruption measures to foster sustainable growth and development. Keywords: Poverty, Corruption, Inflation, Headcount, Poverty gap, Africa, poor, Income, Growth, Index