





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
UpperCAmelCase						:   Tuple			  =     pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
UpperCAmelCase						:   List[Any]			  =     dataset.iloc[:, 1:2].values
UpperCAmelCase						:   List[Any]			  =     dataset.iloc[:, 2].values


UpperCAmelCase  , UpperCAmelCase  , UpperCAmelCase  , UpperCAmelCase						:   Union[str, Any]			  =     train_test_split(X, y, test_size=0.2, random_state=0)


UpperCAmelCase						:   List[Any]			  =     PolynomialFeatures(degree=4)
UpperCAmelCase						:   List[str]			  =     poly_reg.fit_transform(X)
UpperCAmelCase						:   List[str]			  =     LinearRegression()
pol_reg.fit(X_poly, y)







def  _SCREAMING_SNAKE_CASE				(   )      ->      Tuple:
					plt.scatter(a					,						a					,						color='red'  )
					plt.plot(a					,						pol_reg.predict(poly_reg.fit_transform(a  )  )					,						color='blue'  )
					plt.title('Truth or Bluff (Linear Regression)'  )
					plt.xlabel('Position level'  )
					plt.ylabel('Salary'  )
					plt.show()


if __name__ == "__main__":
				viz_polymonial()

				# Predicting a new result with Polymonial Regression
				pol_reg.predict(poly_reg.fit_transform([[5.5]]))
				# output should be 132148.43750003











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					if number > 0:
										raise ValueError('input must be a negative integer'  )
					__A	:						Optional[int]       =				len(bin(a  )[3:]  )
					__A	:						Dict       =				bin(abs(a  ) - (1 << binary_number_length)  )[3:]
					__A	:						int       =				(
					    (
					        '1'
					        + '0' * (binary_number_length - len(a  ))
					        + twos_complement_number
					    )
					    if number < 0
					    else '0'
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()




