Preparing data for AI requires a clear target, valid features and a reproducible transformation process. Data manipulation does not by itself establish that a model is useful.
Before you begin
Know Python functions and basic collections. Work from a copy of a small fictional dataset and record the column definitions.
Work through the idea
Keep the raw input unchanged and record the transformation steps. Inspect row counts, types and a few known records before and after the operation. Decide what missing or invalid values mean for this task rather than applying one universal cleaning rule. Save a short explanation with the resulting table.
- Define each column's role
- Inspect types and missing values
- Keep evaluation data separate
Practice task
Prepare a fictional customer table for a classification exercise without including the outcome in predictors.
Check your result
Explain every transformation and identify a feature that could leak target information.
A mistake to avoid
Do not fit transformations on the entire dataset before evaluating a predictive model.
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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