Using Sklearn Pipelines to Streamline your Machine Learning Process

Machine learning usually involves a number of steps -- load the data, visualize the data, split the data, preprocess the data, and then finally train the model with the training data. All these steps must be followed in sequence, and we usually perform all these steps sequentially in Jupyter Notebook. And before you know it, it is one hell of a mess, with code snippets scattered in various cells. However, all these could be streamlined using sklearn's Pipelineclass, which is a class designed to provide a way to automate your machine learning workflow. In this article, I will explain to you how to use sklearn Pipeline to define and automate your machine learning workflow.