Compare ML Models in few clicks with PyCaret in Python - DEMO
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.
In other words, 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.
PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only.This Python package supports following Machine Learning (ML) models and frameworks:
- scikit-learn
XGBoost
LightGBM
CatBoost
spaCy
Optuna
Hyperopt
Ray, and few more.
In this Demo I showed how to:
Install PyCaret.
Setup PyCaret environment in Jupyter Notebook
Prepare data for analysis.
Compare different ML models for the given dataset
Analyze ML performance metrics
Select the best ML model
Check the model predictions
Save the best model locally on your computer.
Links:
Official PyCaret documentation: https://github.com/pycaret/pycaret
Official PyCare Github repo: https://github.com/pycaret/pycaret
At the moment this video was recorded, there was PyCaret v.2.3 available. The project is updated very smartly and more interactive features will be available soon. Recommend to try it!
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