Bolanle Adegboye

Evaluated and improved machine learning model outputs across computer vision and image-processing tasks, reducing evaluation errors and cutting the retraining feedback loop by 70%. Generated and validated high-quality training data at scale, enabling a leading conversational AI to use 80% of public APIs without technical input from end users.

Applied statistical modeling to insurance claims data to identify risk patterns and support data-driven premium pricing decisions. Built and validated predictive models to estimate claim probability, supporting actuarial risk modeling initiatives.

Collected, digitized, and structured data for roughly 1,150 attendees using Excel and Google Forms, producing analysis-ready datasets. Cleaned and preprocessed data with Power Query and Python (Pandas, NumPy), resolving missing values, duplicates, and structural inconsistencies.

Built and optimized machine learning models with Scikit-learn, using feature engineering and hyperparameter tuning to improve accuracy and generalization. Developed interactive Tableau and Power BI dashboards that communicated insights to non-technical stakeholders.

Configured and maintained network infrastructure (routers, switches, wireless systems) and supported satellite and radio communication, keeping connectivity stable and secure.

Pursuing a Master of Science in Data Science with a thesis on evaluating an agentic multi-agent framework for autonomous data science.

Earned a Bachelor of Engineering in Electrical and Electronics Engineering with a final year project on AI for breast cancer detection.