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An open-source, low-code machine learning library in Python

๐ŸŽ‰๐ŸŽ‰๐ŸŽ‰ PyCaret 3.3 is now available. ๐ŸŽ‰๐ŸŽ‰๐ŸŽ‰

pip install --upgrade pycaret

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Welcome to PyCaret

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.

In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, Optuna, Hyperopt, Ray, and few more.

The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise. PyCaret was inspired by the caret library in R programming language.

๐Ÿš€ Installation

๐ŸŒ Option 1: Install via PyPi

PyCaret is tested and supported on 64-bit systems with:

  • Python 3.9, 3.10 and 3.11
  • Ubuntu 16.04 or later
  • Windows 7 or later

You can install PyCaret with Python's pip package manager:

# install pycaret
pip install pycaret

PyCaret's default installation will not install all the optional dependencies automatically. Depending on the use case, you may be interested in one or more extras:

# install analysis extras
pip install pycaret[analysis]

# models extras
pip install pycaret[models]

# install tuner extras
pip install pycaret[tuner]

# install mlops extras
pip install pycaret[mlops]

# install parallel extras
pip install pycaret[parallel]

# install test extras
pip install pycaret[test]


# install multiple extras together
pip install pycaret[analysis,models]

Check out all optional dependencies. If you want to install everything including all the optional dependencies:

# install full version
pip install pycaret[full]

๐Ÿ“„ Option 2: Build from Source

Install the development version of the library directly from the source. The API may be unstable. It is not recommended for production use.

pip install git+ --upgrade

๐Ÿ“ฆ Option 3: Docker

Docker creates virtual environments with containers that keep a PyCaret installation separate from the rest of the system. PyCaret docker comes pre-installed with a Jupyter notebook. It can share resources with its host machine (access directories, use the GPU, connect to the Internet, etc.). The PyCaret Docker images are always tested for the latest major releases.

# default version
docker run -p 8888:8888 pycaret/slim

# full version
docker run -p 8888:8888 pycaret/full

๐Ÿƒโ€โ™‚๏ธ Quickstart

1. Functional API

# Classification Functional API Example

# loading sample dataset
from pycaret.datasets import get_data
data = get_data('juice')

# init setup
from pycaret.classification import *
s = setup(data, target = 'Purchase', session_id = 123)

# model training and selection
best = compare_models()

# evaluate trained model

# predict on hold-out/test set
pred_holdout = predict_model(best)

# predict on new data
new_data = data.copy().drop('Purchase', axis = 1)
predictions = predict_model(best, data = new_data)

# save model
save_model(best, 'best_pipeline')


# Classification OOP API Example

# loading sample dataset
from pycaret.datasets import get_data
data = get_data('juice')

# init setup
from pycaret.classification import ClassificationExperiment
s = ClassificationExperiment()
s.setup(data, target = 'Purchase', session_id = 123)

# model training and selection
best = s.compare_models()

# evaluate trained model

# predict on hold-out/test set
pred_holdout = s.predict_model(best)

# predict on new data
new_data = data.copy().drop('Purchase', axis = 1)
predictions = s.predict_model(best, data = new_data)

# save model
s.save_model(best, 'best_pipeline')

๐Ÿ“ Modules


Functional API OOP API


Functional API OOP API

Time Series

Functional API OOP API


Functional API OOP API

Anomaly Detection

Functional API OOP API

๐Ÿ‘ฅ Who should use PyCaret?

PyCaret is an open source library that anybody can use. In our view the ideal target audience of PyCaret is:

  • Experienced Data Scientists who want to increase productivity.
  • Citizen Data Scientists who prefer a low code machine learning solution.
  • Data Science Professionals who want to build rapid prototypes.
  • Data Science and Machine Learning students and enthusiasts.

๐ŸŽฎ Training on GPUs

To train models on the GPU, simply pass use_gpu = True in the setup function. There is no change in the use of the API; however, in some cases, additional libraries have to be installed. The following models can be trained on GPUs:

  • Extreme Gradient Boosting
  • CatBoost
  • Light Gradient Boosting Machine requires GPU installation
  • Logistic Regression, Ridge Classifier, Random Forest, K Neighbors Classifier, K Neighbors Regressor, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression requires cuML >= 0.15

๐Ÿ–ฅ๏ธ PyCaret Intel sklearnex support

You can apply Intel optimizations for machine learning algorithms and speed up your workflow. To train models with Intel optimizations use sklearnex engine. There is no change in the use of the API, however, installation of Intel sklearnex is required:

pip install scikit-learn-intelex

๐Ÿค Contributors

๐Ÿ“ License

PyCaret is completely free and open-source and licensed under the MIT license.

โ„น๏ธ More Information

Important Links Description
โญ Tutorials Tutorials developed and maintained by core developers
๐Ÿ“‹ Example Notebooks Example notebooks created by community
๐Ÿ“™ Blog Official blog by creator of PyCaret
๐Ÿ“š Documentation API docs
๐Ÿ“บ Videos Video resources
โœˆ๏ธ Cheat sheet Community Cheat sheet
๐Ÿ“ข Discussions Community Discussion board on GitHub
๐Ÿ› ๏ธ Release Notes Release Notes