Prepare tabular data for reliable modeling by defining features and targets, handling data types and scale, preventing leakage, and building repeatable preprocessing workflows.
Tutorials
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Your model is only as smart as the table you hand it. Most beginners skip straight to fitting an algorithm, then blame the model when results disappoint.…
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Every supervised learning problem is a question about a table. The hard part is knowing which columns the model is allowed to look at.
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The real question is not whether a column contains text or numbers. It is whether the model should treat those values as ordered quantities or as separate…
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You train the same model twice on the same data. The only difference: you scaled the features before the second run. The results are not slightly…
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You train a model. The validation score comes back at 0.98. You feel like a genius. Then the model goes into the real world and performs like a coin flip.
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You fit a scaler on your full dataset, split into training and test sets, train a model, and watch it score beautifully. Then the model meets real data and…
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Every beginner hits the same wall: you load a real dataset, and it is full of holes. Columns you need are dotted with NaN. Your model refuses to run. So…
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Your data is clean. Your rows are ready. Then you feed the model a column of colors—"red", "green", "blue"—and it refuses to train. The error message is…
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One dataset, many feature types, one honest workflow. That is the problem ColumnTransformer solves.
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Every beginner hits the same fork in the road. You plot your data, spot a few points sitting far from the pack, and freeze. Delete them? Keep them? The…
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