Machine learning fits patterns from examples for a defined task. A useful experiment needs a target, baseline, evaluation method and an honest account of limitations.
Before you begin
Know Python, tabular data handling and basic statistics. Use fictional or permitted data and identify the prediction or evaluation task.
Work through the idea
Keep an experiment record with the dataset, split, baseline, metric and limitations. Choose information that would genuinely be available at the time of prediction. Compare the method with a simple alternative and inspect mistakes. A learning experiment should not be presented as a validated production decision system.
- Specify the prediction task
- Split data appropriately
- Compare against a simple baseline
Practice task
Predict a fictional numeric outcome using features available before the outcome occurs.
Check your result
Document the split, metric and one failure case that the model does not handle.
A mistake to avoid
Do not leak the target or future information into input features.
Keep a review note
Save the inputs or scenario, your reasoning and the result you checked. Explain one assumption and one case that needs more work. Revise the exercise after feedback and distinguish what you demonstrated from what remains untested.
Official reference for further study
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