In [21]:

    

from sklearn.datasets import make_regression,make_classification

from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression


    

In [22]:

    

X, y = make_classification(n_samples=100,n_features=10,n_informative=2)

X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)


    

In [23]:

    

X_train.shape, X_test.shape, y_train.shape, y_test.shape


    

    
Out[23]:





((67, 10), (33, 10), (67,), (33,))

In [24]:

    

# it takes a list of tuples as parameter
pipeline = Pipeline([
    ('scaler',StandardScaler()),
    ('clf', LogisticRegression())
])

# use the pipeline object as you would
# a regular classifier
pipeline.fit(X_train,y_train)


    

    
Out[24]:





Pipeline(memory=None,
     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('clf', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])

In [25]:

    

y_preds = pipeline.predict(X_test)


    

In [26]:

    

accuracy_score(y_test,y_preds)


    

    
Out[26]:





0.84848484848484851