

'''simple docstring'''





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor






def 				_lowerCAmelCase			(   __magic_name__						:     dict						)				->  tuple:
						return (data["data"], data["target"])






def 				_lowerCAmelCase			(   __magic_name__						:     np.ndarray   , __magic_name__						:     np.ndarray   , __magic_name__						:     np.ndarray						)				->  np.ndarray:
						lowercase :							Union[str, Any]    		=XGBRegressor(verbosity=0   , random_state=42						)
						xgb.fit(__magic_name__   , __magic_name__						)
						# Predict target for test data
						lowercase :							int    		=xgb.predict(__magic_name__						)
						lowercase :							Tuple    		=predictions.reshape(len(__magic_name__						)   , 1						)
						return predictions






def 				_lowerCAmelCase			(   )				->  None:
						lowercase :							Tuple    		=fetch_california_housing()
						lowercase		,	lowercase :							int    		=data_handling(__magic_name__						)
						lowercase		,	lowercase		,	lowercase		,	lowercase :							Optional[Any]    		=train_test_split(
						    __magic_name__   , __magic_name__   , test_size=0.2_5   , random_state=1						)
						lowercase :							List[str]    		=xgboost(__magic_name__   , __magic_name__   , __magic_name__						)
						# Error printing
						print(f'''Mean Absolute Error : {mean_absolute_error(__magic_name__   , __magic_name__						)}'''						)
						print(f'''Mean Square Error  : {mean_squared_error(__magic_name__   , __magic_name__						)}'''						)


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)
							main()








'''simple docstring'''





def 				_lowerCAmelCase			(   __magic_name__						:     int = 600851475143						)				->  int:

						try:
												lowercase :							Any    		=int(__magic_name__						)
						except (TypeError, ValueError):
												raise TypeError('''Parameter n must be int or castable to int.'''						)
						if n <= 0:
												raise ValueError('''Parameter n must be greater than or equal to one.'''						)
						lowercase :							Optional[Any]    		=2
						lowercase :							Dict    		=0
						if n == 2:
												return 2
						while n > 2:
												while n % i != 0:
																		i += 1
												lowercase :							Union[str, Any]    		=i
												while n % i == 0:
																		lowercase :							Optional[int]    		=n // i
												i += 1
						return int(__magic_name__						)


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





