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 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