PyCaret

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It serves as an end-to-end machine learning and model management tool that exponentially speeds up the experiment cycle and makes data scientists more productive. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks, such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and more.

• Low-Code Approach: Replace hundreds of lines of code with just a few lines, making experiments exponentially fast and efficient. Designed for both experienced data scientists and citizen data scientists who prefer simplified ML solutions.

• Multiple ML Modules: Supports classification, regression, time series forecasting, clustering, and anomaly detection through both Functional and Object-Oriented Programming APIs.

• Deployment Ready: All steps performed in an ML experiment can be reproduced using a pipeline that is reproducible and guaranteed for production. Pipelines can be saved in binary file format that is transferable across environments.

• BI Integration: Seamlessly integrates with environments supporting Python such as Microsoft Power BI, Tableau, Alteryx, and KNIME, allowing users to add machine learning capabilities to existing workflows.

• GPU Training Support: Train models on GPU by simply passing use_gpu = True in the setup function. Supports Extreme Gradient Boosting, CatBoost, Light Gradient Boosting Machine, and various scikit-learn models with cuML.

• Intel Optimization Support: Apply Intel optimizations for machine learning algorithms using the sklearnex engine to speed up workflows.

• Flexible Installation: Install via PyPi with optional dependencies for analysis, models, tuning, MLOps, parallel processing, and testing. Also available via Docker with pre-installed Jupyter notebook.

• Data Preprocessing: Built-in data preprocessing capabilities to prepare datasets for machine learning experiments.

To get started, install PyCaret using pip install pycaret and import the desired module. Use the setup() function to initialize the environment and compare_models() to automatically train and evaluate multiple models. The library provides extensive documentation, tutorials, and video resources for learning.