pycaret

# Import dataset from pycaret repository
from pycaret.datasets import get_data
insurance = get_data('insurance')

# Initialize environment
from pycaret.regression import *
r1 = setup(insurance, target = 'charges', session_id = 123,
           normalize = True,
           polynomial_features = True, trigonometry_features = True,
           feature_interaction=True, 

pycaret

FROM python:3.7

RUN pip install virtualenv
ENV VIRTUAL_ENV=/venv
RUN virtualenv venv -p python3
ENV PATH="VIRTUAL_ENV/bin:$PATH"

WORKDIR /app
ADD . /app

pycaret

FROM python:3.7

RUN pip install virtualenv
ENV VIRTUAL_ENV=/venv
RUN virtualenv venv -p python3
ENV PATH="VIRTUAL_ENV/bin:$PATH"

WORKDIR /app
ADD . /app

pycaret

from pycaret.clustering import *
clu1 = setup(data, ignore_features = ['Country Name', 'Indicator Name'])

pycaret

# import regression module
from pycaret.regression import *

# init setup
reg1 = setup(data, target = 'charges', silent=True, 
            categorical_features=['sex', 'smoker', 'region', 'children'], 
            numeric_features=['age', 'bmi'])

pycaret

#tune with default n_iter i.e. 10
tuned_dt1 = tune_model('dt')

#tune with n_iter = 50
tuned_dt2 = tune_model('dt', n_iter = 50)

pycaret

tuned_lda = tune_model(model='lda', supervised_target='status', estimator='xgboost')

pycaret

# Importing dataset
from pycaret.datasets import get_data

credit = get_data('credit')
# Importing module and initializing setup
from pycaret.classification import *
clf1 = setup(data = credit, target = 'default')

# create a model
xgboost = create_model('xgboost')

pycaret

predict_model(xgboost, probability_threshold=0.2)

pycaret

# import classification module
from pycaret.classification import *

# init setup
clf1 = setup(data, target = 'name-of-target')

# return best model
best = compare_models()

# return best model based on Recall