PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is designed to enhance productivity by speeding up the experiment cycle and reducing the need for extensive coding. Inspired by the caret library in R, PyCaret aims to empower users, including “citizen data scientists,” to perform a range of analytical tasks without requiring advanced technical skills.

The library features a modular structure, with each module addressing specific machine learning use cases. It automates various tasks such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and model deployment. PyCaret integrates with popular machine learning libraries like scikit-learn, XGBoost, and LightGBM, allowing users to leverage their strengths while maintaining a simplified interface. Additionally, it supports model deployment as REST APIs and offers tools for experiment tracking and monitoring to ensure model accuracy over time. PyCaret also has an active community that provides resources, support, and opportunities for collaboration.