Category
Industry
Basically, PyCaret is a low-code tool alternative that can replace hundreds of lines of code with just a few words. It greatly increases the speed of software development and makes it more accessible for beginners. PyCaret is a Python wrapper over several machine learning libraries such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy, and many more.
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.
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.
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.
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.