Build Machine Learning Pipelines( With Code) -- Part 1

There are multiple stages to running machine learning algorithms as it involves a sequence of tasks including pre-processing, feature extraction, model fitting, performance and validation. Pipeline is nothing but a technique through which we create linear sequence of data preparation and modeling steps to automate machine learning workflows. An automated pipeline consists of components and how those components can work together to produce and update the machine learning model. In this post, we are going to create pipeline, find best scalar, estimators and see accuracy score of different machine learning algorithms. We will be using Mines Vs Rocks dataset from Kaggle.