make_column_transformer

make_column_transformer is a function provided by scikit-learn library that allows us to apply different transformers to different columns of a dataset. It is an efficient way of applying multiple transformations to different subsets of the data.

Syntax:

from sklearn.compose import make_column_transformer
make_column_transformer((transformer, columns), ...)

Example:

from sklearn.compose import make_column_transformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

# Define the column transformer
preprocessor = make_column_transformer(
    (StandardScaler(), ['age', 'income']),
    (OneHotEncoder(), ['gender', 'education'])
)

# Apply the preprocessor to the dataset
X_transformed = preprocessor.fit_transform(X)

In the above example, we have defined a column transformer that applies StandardScaler to the columns 'age' and 'income' and OneHotEncoder to the columns 'gender' and 'education'. We then apply the transformer to the dataset X using the fit_transform method.