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

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

from pycaret.utils import version
version()

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

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

compare_models()

lr = create_model('lr')

dt = create_model('dt')

tuned_dt = tune_model('dt')

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

print(tuned_nb)

bagged_dt = ensemble_model(dt)

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

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

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

blender.estimators_

plot_model(blender)

plot_model(blender, plot = 'confusion_matrix')

plot_model(blender, plot = 'threshold')

plot_model(blender, plot = 'pr')

plot_model(tuned_dt, plot = 'vc')

plot_model(dt, plot = 'boundary')

plot_model(tuned_nb, plot = 'boundary')

plot_model(blender, plot = 'boundary')

evaluate_model(tuned_nb)

xgboost = create_model('xgboost')

interpret_model(xgboost)

interpret_model(xgboost, plot = 'correlation')

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

interpret_model(xgboost, plot = 'reason')

deploy_model(xgboost, model_name = 'xgboost-for-aws', authentication = {'bucket' : 'pycaret-test'})