#!/usr/bin/env python
# coding: utf-8

# # Installation

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# run this cell to install pycaret in Google Colab
# !pip install pycaret


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# If you are using Jupyter notebook, you can pip install pycaret using jupyter notebook or command line
# pip install pycaret


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from pycaret.utils import version
version()


# # 1. Importing Dataset

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from pycaret.datasets import get_data
data = get_data('juice')


# # 2. Setting up Environment

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get_ipython().run_line_magic('pinfo2', 'setup')


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from pycaret.classification import *
clf1 = setup(data, target = 'Purchase', session_id=786)


# # 3. Compare Models

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compare_models()


# # 4. Create Model

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lr = create_model('lr')


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dt = create_model('dt')


# # 5. Tune Model

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tuned_dt = tune_model('dt')


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tuned_nb = tune_model('nb', optimize = 'AUC')


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print(tuned_nb)


# # 6. Ensemble Model

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bagged_dt = ensemble_model(dt)


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boosted_dt = ensemble_model(dt, method = 'Boosting')


# # 7. Blend Models

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lr = create_model('lr', verbose=False)
lda = create_model('lda', verbose=False)
gbc = create_model('gbc', verbose=False)


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blender = blend_models(estimator_list=[lr,lda,gbc], method = 'soft')


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blender.estimators_


# # 8. Analyze Model

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plot_model(blender)


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plot_model(blender, plot = 'confusion_matrix')


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plot_model(blender, plot = 'threshold')


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plot_model(blender, plot = 'pr')


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plot_model(tuned_dt, plot = 'vc')


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plot_model(dt, plot = 'boundary')


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plot_model(tuned_nb, plot = 'boundary')


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plot_model(blender, plot = 'boundary')


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evaluate_model(tuned_nb)


# # 9. Interpret Model

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xgboost = create_model('xgboost')


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interpret_model(xgboost)


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interpret_model(xgboost, plot = 'correlation')


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interpret_model(xgboost, plot = 'reason', observation=1)


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interpret_model(xgboost, plot = 'reason')


# # Learning Resources:
# 
# - PyCaret Classification Module : https://www.pycaret.org/classification
# - Binary Classification Tutorial (Level Beginner) : https://pycaret.org/clf101/
# - Binary Classification Tutorial (Level Intermediate) : https://pycaret.org/clf102/
# - Kaggle Titanic Predictions (Video Tutorial) : https://www.youtube.com/watch?v=nqMM6rngNCA