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.

It's rare when we get a dataset without any missing values. Can you remember the last time that happened? It is important to check the number of missing values present in all the columns. Knowing the count helps us treat the missing values before building any machine learning model using that data.

I know You have knowledge of building a machine learning model. It requires many steps like data cleaning, data reduction, model creation, and other steps. Each time you define a problem on it, you repeat all the steps to make a better model. But wait do you know you can automate these steps?. If yes then you can read our other posts.

Last week at its Ignite Conference, Microsoft unveiled the preview version of Automated Machine Learning(ML), a component of Azure ML that allows non-data science experts to build machine learning pipelines. Microsoft's automated machine learning can be seen as their entrance in the popular Auto ML space which is quickly becoming one of the most active areas of research in the machine learning space. The work behind Microsoft's automated machine learning were outlined in a research paper published a few months ago. Building machine learning solutions in the real world is a never-ending cycle of different steps such as extracting features, identifying models, tuning hyperparameters, etc. Each one of these tasks requires specific expertise that results overwhelming to non-data scientists.