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.
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.
PyCaret is a business ready solution. It allows you to do prototyping quickly and efficiently from your choice of notebook environment.
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Short, hands-on and tutorial style stories.
Solution to common ML problems
Simple step-by-step walkthrough tutorials of PyCaret in notebook
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.
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.
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.
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