At Enterprise Mobility, I developed Python scripts to automate repetitive network-monitoring checks, parsing raw logs to process device information. I also transformed device telemetry into clean datasets for performance trend analysis and reporting.
For my loan-default classification project, I designed an end-to-end machine-learning pipeline for 800 applicants. I compared classifiers using stratified cross-validation and hyperparameter tuning, then developed a soft-voting ensemble that achieved 77.5% validation accuracy and a 0.799 ROC-AUC.
My projects also include modelling energy-efficiency trends across 28 countries and 314 local authorities, and estimating macroeconomic responses to interest-rate shocks with VAR models. I’m completing a BSc in Economics and Data Science at Royal Holloway, University of London, and have delivered more than 450 hours of one-to-one STEM tuition.
