atom-data/zindi-financial-challenge

This project explores financial inclusion in Africa by predicting which individuals are most likely to have a bank account, using demographic and socioeconomic data from Kenya, Rwanda, Tanzania, and Uganda. The model is built as part of a Zindi machine learning challenge aimed at addressing financial access disparities. # Project Title: Zindi Financial Inclusion in Africa This project aims to develop a machine learning model that predicts which individuals in Africa are most likely to have or use a bank account. The work is based on a financial inclusion challenge hosted on Zindi, which focuses on four countries: Kenya, Rwanda, Tanzania, and Uganda. Despite the rise of mobile money and fintech solutions in Africa, traditional bank accounts remain a key indicator of financial inclusion. Access to banking services enables individuals and businesses to save, make payments, and access credit, insurance, and other financial services, making it a vital factor in economic and human development. By analysing demographic and socioeconomic data, this project seeks to: Understand key factors that influence whether someone owns a bank account. Provide insights into the financial inclusion landscape across the region. Build a predictive model that can support targeted financial outreach or policy decisions. This project serves both as a learning opportunity in applied machine learning and as a practical exploration of a real-world, socially impactful problem.