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Machine Learning Academy
Author a reviewable job-quantity regression model with explicit features, missing-value handling and deliberate publication.
Checks validate native model configuration and lesson requirements. They do not run SQL, normalize rows, train a model, score predictions or establish predictive quality.
Machine Learning Academy
Choose what you want to predict
Predict a numeric quantity from information available before the job completes.
Scenario 2 · 12 minDefine the training rows
Select only the columns the model needs and make the training cap explicit.
Scenario 3 · 12 minHandle missing features deliberately
Drop rows without priority rather than inventing an arbitrary numeric value.
Scenario 4 · 12 minStart with a bounded baseline
Make a small, reviewable initial configuration before attempting tuning.
Scenario 5 · 12 minAdd a training-data quality gate
Consolidate the model and leave publication for a reviewed training result.
Keep learning
Author a reviewable job-quantity regression model with explicit features, missing-value handling and deliberate publication.