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https://github.com/pycaret/pycaret

An open-source, low-code machine learning library in Python
https://github.com/pycaret/pycaret

anomaly-detection citizen-data-scientists classification clustering data-science gpu machine-learning ml pycaret python regression time-series

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

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

## 🎉🎉🎉 **PyCaret 3.0 is now available. 🎉🎉🎉**

## `pip install --upgrade pycaret` 









  Docs •

  Tutorials •

  Blog •

  LinkedIn •

  YouTube •

    Slack




| Overview | |

|---|---|

| **CI/CD** | ![pytest on push](https://github.com/pycaret/pycaret/workflows/pytest%20on%20push/badge.svg) [![Documentation Status](https://readthedocs.org/projects/pip/badge/?version=stable)](http://pip.pypa.io/en/stable/?badge=stable) |

| **Code** |  [![!pypi](https://img.shields.io/pypi/v/pycaret?color=orange)](https://pypi.org/project/pycaret/) [![!python-versions](https://img.shields.io/badge/Python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)](https://badge.fury.io/py/pycaret) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

| **Downloads**| [![Downloads](https://static.pepy.tech/personalized-badge/pycaret?period=week&units=international_system&left_color=grey&right_color=blue&left_text=weekly%20(pypi))](https://pepy.tech/project/pycaret) [![Downloads](https://static.pepy.tech/personalized-badge/pycaret?period=month&units=international_system&left_color=grey&right_color=blue&left_text=monthly%20(pypi))](https://pepy.tech/project/pycaret) [![Downloads](https://static.pepy.tech/personalized-badge/pycaret?period=total&units=international_system&left_color=grey&right_color=blue&left_text=cumulative%20(pypi))](https://pepy.tech/project/pycaret) |

| **License** | [![License](https://img.shields.io/pypi/l/ansicolortags.svg)](https://img.shields.io/pypi/l/ansicolortags.svg)

| **Community** | [![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/pycaret/shared_invite/zt-20gl4zb8k-L~ZQDyi9LtrV4dWxYpLE7A) |



![alt text](docs/images/quick_start.gif)






# 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.



# 🚀 Installation



## 🌐 Option 1: Install via PyPi

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

- Python 3.8, 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:



```python

# 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:



```python

# 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](https://github.com/pycaret/pycaret/blob/master/requirements-optional.txt). If you want to install everything including all the optional dependencies:



```python

# install full version

pip install pycaret[full]

```

## 📄 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.



```python

pip install git+https://github.com/pycaret/pycaret.git@master --upgrade

```



## 📦 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.



```python

# default version

docker run -p 8888:8888 pycaret/slim



# full version

docker run -p 8888:8888 pycaret/full

```



## 🏃‍♂️ Quickstart



### 1. Functional API

```python

# 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

evaluate_model(best)



# 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')

```



### 2. OOP API



```python

# 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

s.evaluate_model(best)



# 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')

```



## 📁 Modules




## **Classification**



  Functional API           |  OOP API

:-------------------------:|:-------------------------:

![](docs/images/classification_functional.png)  | ![](docs/images/classification_OOP.png)



## **Regression**



  Functional API           |  OOP API

:-------------------------:|:-------------------------:

![](docs/images/regression_functional.png)  | ![](docs/images/regression_OOP.png)



## **Time Series**



  Functional API           |  OOP API

:-------------------------:|:-------------------------:

![](docs/images/time_series_functional.png)  | ![](docs/images/time_series_OOP.png)



## **Clustering**



  Functional API           |  OOP API

:-------------------------:|:-------------------------:

![](docs/images/clustering_functional.png)  | ![](docs/images/clustering_OOP.png)



## **Anomaly Detection**



  Functional API           |  OOP API

:-------------------------:|:-------------------------:

![](docs/images/anomaly_functional.png)  | ![](docs/images/anomaly_OOP.png)






# 👥 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.



# 🎮 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](https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html)

- 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](https://github.com/rapidsai/cuml)



# 🖥️ PyCaret Intel sklearnex support

You can apply [Intel optimizations](https://github.com/intel/scikit-learn-intelex) 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:



```python

pip install scikit-learn-intelex

```



# 🤝 Contributors



  




# 📝 License

PyCaret is completely free and open-source and licensed under the [MIT](https://github.com/pycaret/pycaret/blob/master/LICENSE) license.



# ℹ️ More Information



| Important Links              |            Description                                       |

| -------------------------- | -------------------------------------------------------------- |

| :star: **[Tutorials]**        | Tutorials developed and maintained by core developers       |

| :clipboard: **[Example Notebooks]** | Example notebooks created by community               |

| :orange_book: **[Blog]** | Official blog by creator of PyCaret                      |

| :books: **[Documentation]**      | API docs                              |

| :tv: **[Videos]**            | Video resources             |

| ✈️ **[Cheat sheet]**            | Community Cheat sheet            |

| :loudspeaker: **[Discussions]**        | Community Discussion board on GitHub|

| :hammer_and_wrench: **[Release Notes]**          | Release Notes          |



[tutorials]: https://pycaret.gitbook.io/docs/get-started/tutorials

[Example notebooks]: https://github.com/pycaret/examples

[Blog]: https://pycaret.gitbook.io/docs/learn-pycaret/official-blog

[Documentation]: https://pycaret.gitbook.io/docs/

[Videos]: https://pycaret.gitbook.io/docs/learn-pycaret/videos

[Cheat sheet]: https://pycaret.gitbook.io/docs/learn-pycaret/cheat-sheet

[Discussions]: https://github.com/pycaret/pycaret/discussions

[Release Notes]: https://github.com/pycaret/pycaret/releases