






def 				A	(			a_				,a_   )	->		Any:
					sequence.append(a_   )

					__UpperCamelCase :   Dict									=0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(a_   ):
										return None

					return index


if __name__ == "__main__":
							A_			:Optional[int]	    =		input('''Enter numbers separated by comma:\n''').strip()
							A_			:Any	    =		[int(item) for item in user_input.split(''',''')]

							A_			:Any	    =		input('''Enter a single number to be found in the list:\n''')
							A_			:Optional[int]	    =		int(target_input)
							A_			:List[Any]	    =		sentinel_linear_search(sequence, target)
							if result is not None:
														print(f"{target} found at positions: {result}")
							else:
														print('''Not found''')









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

# Load iris file
A_			:Union[str, Any]	    =		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
A_    ,A_    ,A_    ,A_			:Tuple	    =		train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

A_			:Any	    =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

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

A_			:Union[str, Any]	    =		knn.predict(X_new)

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

