Data Scientists often build Machine learning pipelines which involves preprocessing (imputing null values, feature transformation, creating new features), modeling, hyper parameter tuning. There are many transformations that need to be done before modeling in a particular order. Scikit learn provides us with the Pipeline class to perform those transformations in one go. Pipeline serves multiple purposes here (from documentation ): Convenience and encapsulation : You only have to call fit and predict once on your data to fit a whole sequence of estimators. Joint parameter selection : You can grid search over parameters of all estimators in the pipeline at once (hyper-parameter tuning/optimization). Safety : Pipelines help avoid leaking statistics from your test data into the trained model in cross-validation, by ensuring that the same samples are used to train the transformers and predictors. In this article, I will show you How to build a complete pi
Data Scientist | Tech Blogger