My Projects
These are applied projects that start with a practical question and end with something a decision-maker
could use. They span healthcare access, actuarial, mobility analytics, iGaming, and sports forecasting,
and they draw on
Python, R, SQL, Tableau, Looker, Power BI, and GCP, along with statistical modelling and
machine learning. Each project includes the problem, the approach, and the findings, with the full code in
a public repository so you can see how I work as well as what I found.
Healthcare · Python
Mapping healthcare access gaps
Access to care varies widely across Nairobi, and averages hide where the shortfalls are. This project
compares sub-counties on facility access, bed availability, and selected health services, measured
against population size and recommended target ratios.
The analysis highlights which areas are underserved and by how much several sub-counties fall
below the UN Department of Economic and Social Affairs SDG-recommended targets,
giving planners and health organisations an evidence base for prioritising resources.
Actuarial · Python
Claims reserving with chain ladder
Insurers need to estimate what claims will ultimately cost before they are fully settled. Using payment
data, I built a claims development triangle, calculated loss development factors, and projected ultimate
losses with the chain ladder method. The project walks through each step of the reserving process, from
data preparation to the final reserve estimate, in clear, reproducible code.
Mobility · Time series · Python
EV battery performance analysis
Electric vehicles are a cornerstone of cleaner transport globally. In East Africa, electric motorcycles
(boda bodas) are especially promising as public transport, carrying passengers across both busy urban
centres and rural areas where other options are limited. Their climate benefit, however, depends on how
sustainable they are, i.e. how long the batteries last and how well fleets are run.
I analysed battery swap and telemetry data to understand how batteries degrade with use, then built time
series forecasts of fleet usage.
findings on degradation patterns and usage trends show how operators could plan swap capacity,
maintenance, and replacement more effectively. Longer battery life means fewer batteries manufactured
and discarded, and better-planned swapping means less idle infrastructure, which makes electric
mobility more economical and more sustainable.
Sports · ML · Python
Football match outcome predictor
A web app that estimates match outcomes across leagues and teams. Instead of treating all history
equally, the model uses recency-weighted performance, so recent form counts for more than results from
seasons ago. It shows an end-to-end workflow from data and modelling through to a usable interface.
Actuarial · R
Claims reserving in R
A companion to the Python reserving project, implementing the same chain ladder approach in R. Having
both versions allows a direct comparison of the two workflows and shows the method working in either
language, which is useful for teams whose actuarial work is split between them.