Scaling and normalization choices for different model families

                  from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
from sklearn.linear_model import LogisticRegression

standard_pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('model', LogisticRegression(max_iter=1000)),
])

robust_pipeline = Pipeline([
    ('scaler', RobustScaler()),
    ('model', LogisticRegression(max_iter=1000)),
])

minmax_pipeline = Pipeline([
    ('scaler', MinMaxScaler()),
    ('model', LogisticRegression(max_iter=1000)),
])

print(standard_pipeline)
print(robust_pipeline)
print(minmax_pipeline)