Financial services / Data science·Nedbank (public competition)

3rd place, Nedbank Machine Learning & Data Science Challenge 2026

3rd / 251
national ranking
Top 1.2%
of all entrants
End-to-end
data to model

Context

The Nedbank Machine Learning & Data Science Challenge 2026 is a national competition that puts entrants against a real predictive problem, judged on measurable model performance rather than opinion, exactly the evidence-led principle Nullius is built on.

The problem

The challenge required taking raw, imperfect data and producing a model that performed under objective evaluation, competing against 251 entrants including established teams.

Approach

We worked the problem end to end: exploring and cleaning the data, framing the target carefully, engineering features, and iterating across models with honest, held-out evaluation rather than chasing leaderboard noise. Decisions were driven by what the validation data actually showed.

Tech stack

Pythonpandasscikit-learnXGBoostJupyter

Outcome

A 3rd-place finish out of 251 entrants (top 1.2%), demonstrating the ability to take a real, messy data problem and produce a model that performs under objective scrutiny.

Published June 2026

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