





'''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(  UpperCamelCase__    : str		):
    return (data["data"], data["target"])
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : Dict    ,       UpperCamelCase__    : List[Any]		):
    A       :       List[str]							=						XGBRegressor(verbosity=0    ,       random_state=42		)
    xgb.fit(UpperCamelCase__    ,       UpperCamelCase__		)
    # Predict target for test data
    A       :       Union[str, Any]							=						xgb.predict(UpperCamelCase__		)
    A       :       Any							=						predictions.reshape(len(UpperCamelCase__		)    ,       1		)
    return predictions
def 							_lowerCamelCase(  ):
    A       :       Dict							=						fetch_california_housing()
    A,	A       :       Tuple							=						data_handling(UpperCamelCase__		)
    A,	A,	A,	A       :       List[str]							=						train_test_split(
        UpperCamelCase__    ,       UpperCamelCase__    ,       test_size=0.2_5    ,       random_state=1		)
    A       :       Dict							=						xgboost(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
    # Error printing
    print(F'''Mean Absolute Error : {mean_absolute_error(UpperCamelCase__    ,       UpperCamelCase__		)}'''		)
    print(F'''Mean Square Error  : {mean_squared_error(UpperCamelCase__    ,       UpperCamelCase__		)}'''		)


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()







'''simple docstring'''






from math import pi
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : int		)		->  float:
    return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(90, 10))
