Every Data Scientist Should Use PyCaret

Whereas data scientists in the past have had to use quite a bit of code to come up with testing, comparing, and evaluating machine learning algorithms, there has recently been an emergence of libraries in Python that reduce that work significantly. One of those libraries is PyCaret [2], by Moez Ali, an open-source library with small amounts of code required that ultimately allows you to quickly prepare data to deploy your final model in minutes. There are several benefits, which are native to the functions of PyCaret. Some of those benefits include ease of use, efficiency, and learning about new machine learning algorithms. In addition to those more board benefits, there are also around four main steps that all PyCaret models follow that serve as easy ways to execute a process that otherwise, could take more time without this library.