Machine learning and statistics with python

Introduction: In this last post , we discussed the basics of pyspark. Now, in this post, we will discuss how to do machine learning in pyspark. We will discuss what are the main machine learning pipeline elements, and how to use them too. The content is taken from datacamp ditto, and the sole credit of writing the blocks go to datacamp pyspark course. I am merely compiling it together for you to go through fast and learn quickly, with the completed exercises and the full flow. Machine Learning Pipelines You'll step through every stage of the machine learning pipeline, from data intake to model evaluation. Let's get to it! At the core of the pyspark.ml module are the Transformer and Estimator classes. Almost every other class in the module behaves similarly to these two basic classes. Transformer classes have a .transform() method that takes a DataFrame and returns a new DataFrame; usually the original one with a new column appended. For example, you might use t