pkg_pyknnclassifier.scaling

Module Contents

Functions

scaling(train_X, impute_strategy, scale_method)

Apply imputation and scaling to the given data.

pkg_pyknnclassifier.scaling.scaling(train_X, impute_strategy, scale_method)

Apply imputation and scaling to the given data.

Parameters:

• train_X (pd.DataFrame) – The features DataFrame to be preprocessed.

• impute_strategy (str) – The strategy for imputation, ‘mean’, ‘median’, ‘most_frequent’, or ‘constant’.

• scale_method (str) – The scaling method, either ‘StandardScaler’ or ‘MinMaxScaler’.

Returns:

The scaled features DataFrame.

Return type:

pd.DataFrame

Raises:

ValueError – If input types or values are not valid.

Examples

train_data = pd.DataFrame({

‘feature1’: [1, 2, None, 4, 5], ‘feature2’: [3, 4, 5, None, 7]

})

imputed_scaled_data = scaling(train_data, impute_strategy=’mean’, scale_method=’StandardScaler’) print(imputed_scaled_data)