


'''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 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: dict ):






			"""simple docstring"""




			return (data["data"], data["target"])






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: np.ndarray,_SCREAMING_SNAKE_CASE					: np.ndarray,_SCREAMING_SNAKE_CASE					: np.ndarray ):






			"""simple docstring"""




			__A=	XGBRegressor(verbosity=0,random_state=42 )
			xgb.fit(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			# Predict target for test data
			__A=	xgb.predict(_SCREAMING_SNAKE_CASE )
			__A=	predictions.reshape(len(_SCREAMING_SNAKE_CASE ),1 )
			return predictions






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			__A=	fetch_california_housing()
			__A,	__A=	data_handling(_SCREAMING_SNAKE_CASE )
			__A,	__A,	__A,	__A=	train_test_split(
			    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,test_size=0.25,random_state=1 )
			__A=	xgboost(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			# Error printing
			print(f"""Mean Absolute Error : {mean_absolute_error(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )}""" )
			print(f"""Mean Square Error  : {mean_squared_error(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )}""" )


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()







'''simple docstring'''





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "" ):






			"""simple docstring"""




			__A=	url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
			__A=	BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text,'html.parser' )
			__A=	soup.find_all('td',attrs='titleColumn' )
			__A=	soup.find_all('td',class_='ratingColumn imdbRating' )
			return {
			    title.a.text: float(rating.strong.text )
			    for title, rating in zip(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			}






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "IMDb_Top_250_Movies.csv" ):






			"""simple docstring"""




			__A=	get_imdb_top_aaa_movies()
			with open(_SCREAMING_SNAKE_CASE,'w',newline='' ) as out_file:
						__A=	csv.writer(_SCREAMING_SNAKE_CASE )
						writer.writerow(['Movie title', 'IMDb rating'] )
						for title, rating in movies.items():
									writer.writerow([title, rating] )


if __name__ == "__main__":
					write_movies()



