/Machine-Learning-using-PyCaret

Primary LanguageJupyter Notebook

PyCaret for Machine Learning

  • It is a bundle of many Machine Learning algorithms.
  • Only three lines of code is required to compare 20 ML models.
  • Pycaret is available for:
    • Classification
    • Regression
    • Clustering

1. Self Learning Resource

Tutorial on Pycaret for Regression, Classification and Clustering Click Here

2. In this tutorial we will learn

  • Getting Data: How to import data from PyCaret repository
  • Setting up Environment: How to setup an experiment in PyCaret and get started with building regression/classfication/clustering models
  • Create Model: How to create a model, perform cross validation and evaluate regression metrics
  • Tune Model: How to automatically tune the hyperparameters of a regression model
  • Plot Model: How to analyze model performance using various plots
  • Finalize Model: How to finalize the best model at the end of the experiment
  • Predict Model: How to make prediction on new / unseen data
  • Save / Load Model: How to save / load a model for future use

3. Three line of code for model comparison for "Insurance" dataset

from pycaret.datasets import get_data
from pycaret.regression import *

insuranceDataSet = get_data("insurance")
s = setup(data = insuranceDataSet, target='charges', silent=True)
cm = compare_models()

4. Outcome for Regression

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5. Outcome for Classification

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6. Outcome for Clustering

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