


"""simple docstring"""






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
__snake_case				=				pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
__snake_case				=				dataset.iloc[:, 1:2].values
__snake_case				=				dataset.iloc[:, 2].values


__snake_case				=				train_test_split(X, y, test_size=0.2, random_state=0)


__snake_case				=				PolynomialFeatures(degree=4)
__snake_case				=				poly_reg.fit_transform(X)
__snake_case				=				LinearRegression()
pol_reg.fit(X_poly, y)
def 					A_      (      ):






     """simple docstring"""

     plt.scatter(lowercase_,   lowercase_,   color='''red'''				)
     plt.plot(lowercase_,   pol_reg.predict(poly_reg.fit_transform(lowercase_				)				),   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





_lowerCamelCase	:		Optional[int]							       =   65521







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       int:



       """simple docstring"""

       A__       =    1
       A__       =    0
       for plain_chr in plain_text:
              A__       =    (a + ord(lowercase_						)) % MOD_ADLER
              A__       =    (b + a) % MOD_ADLER
       return (b << 16) | a


