# 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 3.7, 3.8, 3.9, and 3.10 - 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