A prototype for decision support
A prediction-tool prototype: a data pipeline feeds a model, and a decision-support interface turns its output into something a non-technical user can act on.
- Starting point
- A recurring decision rested on judgement and scattered history, with no consistent way to compare cases.
- What we built
- A prototype: a data pipeline, a prediction model and a screen a non-technical user can act on.
- What changed
- The decision starts from the same data every time. It is a prototype, not a production system.
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Historical records. Why it helps: Decisions rest on what really happened.
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Cleans the records and feeds them to the model. Why it helps: Clean data in means results you can trust.
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A prediction model trained on that history. Why it helps: Spots patterns a person would miss in the numbers.
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The screen shows the result; a person decides. Why it helps: The model advises; the responsibility stays with people.
Starting point
A recurring kind of decision relied on judgement and scattered historical data, with no consistent way to weigh a new case against earlier ones.
What we built
A data prototype in two parts: a pipeline that cleans historical records and feeds a prediction model, and an interface that shows the result in a form a non-technical user can act on. The parts are separate, so the model can be retrained or replaced without rebuilding the interface.
What changed
The decision starts from a consistent, data-backed view instead of memory and manual comparison. As a prototype, it shows how a production version would work.
What a production version would add
Regular checks that the recommendations stay reliable, and the upkeep a system needs in daily use.
Built with
- Data pipeline that ingests and cleans historical records
- Prediction model trained on that data
- Decision-support interface for a non-technical user