




from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_lowerCamelCase					:     Tuple				       =    load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
_lowerCamelCase					:     Tuple				       =    train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

_lowerCamelCase					:     List[str]				       =    KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_lowerCamelCase					:     Any				       =    [[1, 2, 1, 4], [2, 3, 4, 5]]

_lowerCamelCase					:     List[Any]				       =    knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)











"""simple docstring"""

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						9

	while num > 0:
		if num % 10 != digit:
			return False
		num //= 100
		digit -= 1

	return True

def 						lowercase_ (						):




	"""simple docstring"""






	A_					:			Dict						=						1_3890_2663

	while not is_square_form(num * num		):
		if num % 10 == 3:
			num -= 6  # (3 - 6) % 10 = 7
		else:
			num -= 4  # (7 - 4) % 10 = 3

	return num * 10


if __name__ == "__main__":
		print(f'{solution() = }')





