from sklearn.datasets import load_iris

from evaluate import evaluate_classification_models
import pandas as pd
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

print("{:^28}".format("*IRIS DATASET*"))
print("-----------------------------")

iris = load_iris()

X = pd.DataFrame(iris["data"], columns=iris["feature_names"])
y = iris["target"]

evaluate_classification_models(X, y)

a264102754880f742f5d6907ff27d4437710c679
<mask token>

<mask token>
warnings.filterwarnings('ignore', category=FutureWarning)
print('{:^28}'.format('*IRIS DATASET*'))
print('-----------------------------')
<mask token>
evaluate_classification_models(X, y)

<mask token>
warnings.filterwarnings('ignore', category=FutureWarning)
print('{:^28}'.format('*IRIS DATASET*'))
print('-----------------------------')
iris = load_iris()
X = pd.DataFrame(iris['data'], columns=iris['feature_names'])
y = iris['target']
evaluate_classification_models(X, y)

from sklearn.datasets import load_iris
from evaluate import evaluate_classification_models
import pandas as pd
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
print('{:^28}'.format('*IRIS DATASET*'))
print('-----------------------------')
iris = load_iris()
X = pd.DataFrame(iris['data'], columns=iris['feature_names'])
y = iris['target']
evaluate_classification_models(X, y)

from sklearn.datasets import load_iris

from evaluate import evaluate_classification_models
import pandas as pd
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

print("{:^28}".format("*IRIS DATASET*"))
print("-----------------------------")

iris = load_iris()

X = pd.DataFrame(iris["data"], columns=iris["feature_names"])
y = iris["target"]

evaluate_classification_models(X, y)
