

'''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'''



import sys

__UpperCamelCase   :							List[str]         =   (
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str = N     )	->      int:

   """simple docstring"""
   __a					  =     -sys.maxsize - 1
   for i in range(len(SCREAMING_SNAKE_CASE__     ) - 12     ):
      __a					  =     1
      for j in range(13     ):
         product *= int(n[i + j]     )
      if product > largest_product:
         __a					  =     product
   return largest_product


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