





def     UpperCAmelCase_						(						_UpperCAmelCase       :list     ,	_UpperCAmelCase       :list							)  ->							Union[str, Any]:
     '''simple docstring'''


     A_					      =					len(lowercase__							)
     A_					      =					len(lowercase__							)
     A_					      =					(
         first_input_list_length
         if first_input_list_length > second_input_list_length
         else second_input_list_length
     )
     A_					      =					[]
     for char_count in range(lowercase__							):
          if char_count < first_input_list_length:
               output_result_list.append(first_input_list[char_count]							)
          if char_count < second_input_list_length:
               output_result_list.append(second_input_list[char_count]							)

     return output_result_list


if __name__ == "__main__":
      print(alternative_list_arrange(['A', 'B', 'C'], [1, 2, 3, 4, 5]), end=' ')










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

# Load iris file
a__			:    List[str]			      =		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__			:    Any			      =		train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

a__			:    List[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]}'''
)



