Pipeline was previously used for preprocessing, but its real purpose is to chain preprocessing with a final predictor. The last step in a pipeline can be any estimator (typically a model) that produces predictions.

Why .transform()?

Using .fit_transform() on new data could change encodings (e.g., in OneHotEncoder), creating mismatched columns and unreliable predictions. .transform() guarantees consistent preprocessing, ignoring unseen categories and keeping the same column order.

Here is how one-hot encoded training data looks like:

Here are the new instances to predict:

If .fit_transform() were applied to new instances, the OneHotEncoder could generate columns in a different order or even introduce new ones. This would cause the new data to be transformed inconsistently with the training set, making predictions unreliable.

However, using .transform() ensures that the new data is encoded exactly as the training data, ignoring categories not seen during training:

Adding the Final Estimator

Simply add the model as the last step of the pipeline:

pipe = make_pipeline(
    ct,
    SimpleImputer(strategy='most_frequent'),
    StandardScaler(),
    KNeighborsClassifier()
)
pipe.fit(X, y)
pipe.predict(X_new)

This allows the whole workflow—preprocessing + prediction—to run with one call.