Supervised ML experiments covering data preparation, model evaluation and structured comparison of results.
The problem
Classification experiments need repeatable preparation, evaluation and comparison, and must not be misrepresented as clinical validation.
What was built
A notebook-based supervised machine-learning workflow for cancer classification, with structured result output.
Technical approach
- Prepare the classification data
- Run supervised model experiments
- Evaluate the outputs
- Record the results in a structured form
- Compare how the experiments behave
Engineering decisions
- Frame the work as ML/R&D only.
- Avoid diagnostic, clinical, medical-device or deployment language.
- Hold back model details and metrics until the notebook has been inspected.
How it was checked
- The repository contains a dedicated cancer-classification notebook and a results.csv.
What it shows
Repeatable supervised-learning experiments, with disciplined evaluation.
About the source
Academic and R&D classification project. It is not presented as a diagnostic system, clinical application or medical device.