Why this matters
When you have no machine learning job on your CV yet, your portfolio is your experience. A reviewer at a Ghanaian startup or a remote team will spend a few minutes on it. In that time they want to see that you can frame a problem, handle messy data, evaluate honestly and explain clearly. Two or three strong projects beat ten weak ones.
What makes a strong project
Each project should have:
- A clear question that matters to someone: "Can we estimate maize yield per acre from rainfall, fertiliser and planting date, well enough to help a cooperative plan storage?"
- Real or realistic data, with its source and licence stated. Ghanaian or African datasets make your work stand out and show you understand local problems.
- A baseline and at least one model that beats it, or an honest note that none did.
- Honest evaluation on held out data, with a measure that fits the problem and results broken down by group where it matters.
- A short write up at the top: the question, the data, what you did, what you found, the limits, and what you would do next.
- Clean, runnable code, so someone can rerun your notebook from top to bottom.