imputer =sklearn.impute.simpleImputer()
clf=sklearn.linear_model.logisticRegression()
pip=make_pipline(imputer, clf)
pip.fit(X_train, y_train)
y_pred=pip.predict(X_test)

Want to chain multiple steps into one so that the data will go through a fixed sequence of steps.  try pipelines in SKlearn. Thus instead of calling every step separately, the pipeline concatencates  all steps into one system. To create such a pipline we make use of the make_pipline function . We can use the pipline to fit our train data and to make prediction for the test data