





import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	return (data["data"], data["target"])


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			int											=	XGBClassifier()
	classifier.fit(__a   , __a   )
	return classifier


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Dict											=	load_iris()
	snake_case_		:			int											=	data_handling(__a   )
	snake_case_		:			Tuple											=	train_test_split(
	    __a   , __a   , test_size=0.25   )

	snake_case_		:			str											=	iris['target_names']

	# Create an XGBoost Classifier from the training data
	snake_case_		:			int											=	xgboost(__a   , __a   )

	# Display the confusion matrix of the classifier with both training and test sets
	ConfusionMatrixDisplay.from_estimator(
	    __a   , __a   , __a   , display_labels=__a   , cmap='Blues'   , normalize='true'   , )
	plt.title('Normalized Confusion Matrix - IRIS Dataset'   )
	plt.show()


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()








import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		"""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

						print("""Googling.....""")

						_SCREAMING_SNAKE_CASE      	=		F'''https://www.google.com/search?q={query}&num=100'''

						_SCREAMING_SNAKE_CASE      	=		requests.get(
						    url,
						    headers={"""User-Agent""": str(UserAgent().random)},
						)

						try:
												_SCREAMING_SNAKE_CASE      	=		(
												    BeautifulSoup(res.text, """html.parser""")
												    .find("""div""", attrs={"""class""": """yuRUbf"""})
												    .find("""a""")
												    .get("""href""")
												)

						except AttributeError:
												_SCREAMING_SNAKE_CASE      	=		parse_qs(
												    BeautifulSoup(res.text, """html.parser""")
												    .find("""div""", attrs={"""class""": """kCrYT"""})
												    .find("""a""")
												    .get("""href""")
												)["""url"""][0]

						webbrowser.open(link)

