Why PyCaret

PyCaret is an open source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of notebook environment.

Increased Productivity

PyCaret being a low-code library makes you more productive. With less time spent coding, you and your team can now focus on business problems.

N

Easy to Use

PyCaret is simple and easy to use machine learning library that will help you to perform end-to-end ML experiments with less lines of code. 

Business Ready

PyCaret is a business ready solution. It allows you to do prototyping quickly and efficiently from your choice of notebook environment.

Read our latest stories

Short, hands-on and tutorial style stories.  

Announcing PyCaret 2.0

We are excited to announce the second release of PyCaret. PyCaret is an open source, low-code machine learning library in Python that automates machine learning workflow. 

AutoML in Power BI

Learn how PyCaret can be used to build an Automated Machine Learning Solution within Microsoft Power BI without any additional license or software costs.

GitHub is the best AutoML

Learn how to build your personalized Automated Machine Learning software using PyCaret 2.0 and host it on GitHub for free using GitHub Actions.

Solution to common ML problems

Simple step-by-step walkthrough tutorials of PyCaret in notebook

Credit Card Default Prediction

Train a Classification Model to predict credit card default using demographic factors, credit transaction data, payment history, and billing statements of credit card clients in Taiwan from April 2005 to September 2005.

Diamond Price Prediction

Based on a business case study “Sarah Gets a Diamond”, in this tutorial train a Regression model to predict the price of a diamond using several attributes such as Carat Weight, Cut , Color, Clarity, Polish and Symmetry.

Mice Protein Expression

Train a K-Means clustering model on classes of mice with Down Syndrome exposed to context fear conditioning, a task used to assess associative learning. Each instance in the dataset has 77 observations.

Video Tutorials

Get started with help of bite-sized, short video tutorials. They are extremely easy and perfect for beginners to get started with PyCaret.

Analyze Model Performance

Analyzing the performance of a trained machine learning model is very critical step in the machine learning workflow. With over 60 plots available in PyCaret, you can now evaluate and explain model performance and results instantaneously without the need to write complex code.

Data Preparation in PyCaret

Whether its imputing missing values, transforming categorical data, feature engineering or even hyperparameter tuning of models, PyCaret automates all of it. It orchestrates the entire pipeline no matter how complex it is. 

PyCaret integrates seamlessly

"PyCaret lets us implement a wide array of machine learning techniques in analysis and in production, despite having a small team."

– Jeff Bradshaw

Would you like to contribute to PyCaret?

 

Support

 

Issue Tracker

Frequently Asked Questions

Stack Overflow

Release notes

 

 

Stay Connected

 

Git

Linkedin

Blog

Youtube

 

 

Community

 

Contribute

Testimonials

Mailing list

Donate us