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How do I prepare my data for AI and machine learning projects?
Make sure data is accurate, consistently defined, centralised and has enough history. Document it, clarify the outcome you want to predict, check for bias and gaps, and confirm you are allowed to use it for this purpose. Foundations matter more than the algorithm.
Preparation steps:
- Define the target outcome precisely
- Combine relevant sources into one dataset
- Clean and standardise records
- Check history covers different conditions, such as seasons
- Keep a clear record of how the data was produced
Many AI projects fail because of poor data, not poor algorithms. Involving the people who understand how the data was recorded often reveals quirks that no algorithm would detect.
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