Often in Machine Learning and Data Science, you need to perform a sequence of different transformations of the input data (such as finding a set of features or generating new features) before applying a final estimator. Pipeline gives you a single interface for encapsulating transformers and predictors to simplify the process. Since transformers are usually combined with estimators for preprocessing, using pipeline in scikit-learn can be really useful. To be precise, Pipelines sequentially apply a list of transformers and a final estimator. Therefore, the purpose of the pipeline is to assemble several steps that can be cross-validated while setting different parameters. In this video, we discuss the Python implementation of Pipeline using a polynomial regression example. #Transformer #Estimator #Pipeline