Nigerian Lady Develops Explainable Machine Learning Model for E-commerce

Deborah Okoli, a Nigerian PhD student in Applied Mathematics at Mississippi State University, has created a machine learning system designed to provide easy-to-understand forecasting tools for online markets.

Her research focuses on developing models that can be trusted and questioned by leaders, particularly in the fast-paced world of e-commerce.

Okoli's model examines economic drivers such as labor productivity, research and development, sales, employment, and capital investment to predict online market growth.

The model uses techniques like feature attribution and partial dependence plots to reveal how each variable influences the forecast and whether the impact is immediate or delayed.

Okoli ensures the accuracy of her model by using rolling-window cross-validation, stability checks, and residual diagnostics.

Okoli's goal is to build machine learning models that leaders can trust and understand. She believes that when machine learning becomes easy to understand, it becomes trustworthy and useful. Her work has the potential to empower businesses to make smarter, data-driven decisions in e-commerce.