Main Research Projects

Alumnode Project; Breaking the STEM Gap in Rural Africa This project was a blend of a leadership and career development and an advocacy and science communication project. It was thus a one-day miniature Heidelberg Laureate Forum organized at the Radach conference center in Tamale in the Northern Region of Ghana. In line with the theme of the project, the activities of the conference included career development to provide career guidance for high school students, mentorship, exhibition of science projects and speeches from distinguished scientists.

Mastercard Foundation Scholars Research; Increasing women in STEM Transistion into Tertiary Education In Africa, a chunk of the population are women yet they have the least enrollment figures as you go up the academic stages. This means that a size able chunk of the potential human resource on the continent remain underdeveloped. This quantitative research primarily seeks to identify the challenges women in STEM face while pursuing courses in such fields, it also explores the ways to facilitate access to higher education without comprising on their family obligations.

Financial Inclusion Predicting Financial-Inclusion-Africa(Classification, Explainable AI) Financial Inclusion remains one of the main obstacles to economic and human development in Africa. For example, across Kenya, Rwanda, Tanzania, and Uganda only 9.1 million adults (or 13.9% of the adult population) have access to or use a commercial bank account. Traditionally, access to bank accounts has been regarded as an indicator of financial inclusion. Despite the proliferation of mobile money in Africa and the growth of innovative fintech solutions, banks still play a pivotal role in facilitating access to financial services. The objective of this competition is to create a machine learning model to predict which individuals are most likely to have or use a bank account.

A CNN Based Dog Breed Classifier In this project, I defined a Convolutional Neural Network that is able to classifiy animals either as cats or dogs depending on which breed it is. || || Github ||

An Xgboost Classifier Approach: Predicting Churning in a Telecommunication Industry Expresso is an African telecommunications company that provides customers with airtime and mobile data bundles. The objective of this challenge is to develop a machine learning model to predict the likelihood of each Expresso customer “churning,” i.e. becoming inactive and not making any transactions for 90 days. This solution will help Expresso to better serve their customers by understanding which customers are at risk of leaving.