If you are looking for a Python library to train and deploy supervised and unsupervised machine learning models in a low-code environment, then you should try PyCaret. From data preparation to model deployment, PyCaret allows all these processes in minimum time using your choice of notebook environment.

PyCaret enables data scientists and data engineers to perform end-to-end experiments quickly and efficiently. While most of the open-source machine learning libraries require complex lines of codes, PyCaret is a useful low-code library that can increase the performance in complex machine learning tasks with only a few lines of code. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn , XGBoost , Microsoft LightGBM , spaCy , and many more.

As it requires the least number of lines of code, PyCaret is very simple and easy to use. All the steps performed in a machine learning (ML) experiment can be replicated using a pipeline that is developed automatically in PyCaret as you progress through the experiment.

Installation:

For command line interface or notebook environment

pip install pycaret

For Azure notebooks or Google Colab,

!pip install pycaret

Github: https://github.com/pycaret/pycaret

Tutorial: https://pycaret.org/tutorial/

Blog: https://towardsdatascience.com/announcing-pycaret-an-open-source-low-code-machine-learning-library-in-python-4a1f1aad8d46