Turn a tutorial notebook into a defensible portfolio project

Completing a tutorial proves that you can follow its path. A portfolio project must show which decisions you made, what evidence challenged them and whether another person can reproduce the result. Changing the colour palette or swapping a classifier does not establish that independence.

Audit the missing evidence

The career evidence lab evaluates an invented notebook containing algorithm code, one accuracy value and a chart.

from career_cases import tutorial_defence_case

result = tutorial_defence_case()
assert result["initial_coverage"] == 0
assert result["rebuilt_coverage"] == 8
assert result["remaining_gaps"] == []
print(result["required"])

Those tutorial elements are not useless; they simply do not cover the eight required artifacts: decision brief, data provenance, split manifest, naive baseline, held-out results, error slices, reproduction command and limitations.

Rebuild the question first

State the intended user and action. Define the prediction timestamp and which fields are available then. Record where the data came from, its licence, row grain and known selection bias. If the original tutorial silently supplies a cleaned matrix, reconstruct or explicitly document the missing data work.

Choose a split from the deployment scenario. Keep later periods later for a future-facing prediction; keep the same person or account in one split when repeated entities could leak identity. Fit preprocessing on training rows only. Preserve a simple baseline so a complex candidate must earn its place.

Make your decisions visible

Move reusable transformations into functions or modules and run the project with one command. Save parameters and environment requirements. Export metrics and predictions to machine-readable files rather than leaving the only evidence in rendered notebook output.

Add at least one adverse result. Show a subgroup where recall weakens, a feature that was rejected for leakage, a candidate that lost to the baseline or an assumption that the data cannot verify. A project becomes more credible when the reviewer can see how it could fail.

Credit the tutorial and dataset. Explain what you retained and what you independently changed. Do not present copied structure as original work or invent business impact from an offline exercise.

The Data Science course asks learners to connect notebooks to evaluated and reproducible project evidence. The portfolio claim should describe exactly what was verified.

Exercise

Take one completed tutorial and create an eight-row evidence checklist. Rebuild two missing items without changing the model. Then change one substantive decision—target, split, baseline or evaluation—and document why the result changed.

Continue learning

This article is part of the Data science careers and portfolio decisions sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Choose a data science project from a business decision.
• Continue with Explain model validation in a data science interview.

Reference: scikit-learn guidance on common pitfalls.