While working on a machine learning problem wouldn’t it be better if we can make a quick comparison of a few models which would in turn help us in deciding to which model do we dedicate our time and resources. PyCaret is a library which helps in doing precisely that. The same can be done by even scikit-learn but what sets PyCaret apart is that it gives us a low code version to work on which speeds up the process. But it is not limited to prototyping, but also can be used to develop and deploy a full scale machine learning model.

As we see in figure above, PyCaret provides us the entire gamut of operations for machine learning. We would run through all these steps in PyCaret and would be using the Titanic Dataset for a classification problem. PyCaret can be used to model both supervised and unsupervised problems. Here we would cover supervised — classification to get a gist of the PyCaret library.

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