


'''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
__UpperCAmelCase    :Union[str, Any]				       =     pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
__UpperCAmelCase    :Tuple				       =     dataset.iloc[:, 1:2].values
__UpperCAmelCase    :Optional[int]				       =     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[str]				       =     PolynomialFeatures(degree=4)
__UpperCAmelCase    :Dict				       =     poly_reg.fit_transform(X)
__UpperCAmelCase    :List[Any]				       =     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



'''simple docstring'''



from __future__ import annotations




def 			_a			(		_lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    [True] * limit
     __UpperCAmelCase			:				Tuple													=    False
     __UpperCAmelCase			:				List[str]													=    False
     __UpperCAmelCase			:				List[Any]													=    True

     for i in range(3    ,      int(limit**0.5 + 1						)    ,      2						):
          __UpperCAmelCase			:				Dict													=    i * 2
          while index < limit:
               __UpperCAmelCase			:				Optional[int]													=    False
               __UpperCAmelCase			:				Union[str, Any]													=    index + i

     __UpperCAmelCase			:				Optional[Any]													=    [2]

     for i in range(3    ,      _lowercase    ,      2						):
          if is_prime[i]:
               primes.append(_lowercase						)

     return primes




def 			_a			(		_lowercase      :			int = 1000000						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    prime_sieve(_lowercase						)
     __UpperCAmelCase			:				Optional[Any]													=    0
     __UpperCAmelCase			:				int													=    0

     for i in range(len(_lowercase						)						):
          for j in range(i + length    ,      len(_lowercase						)						):
               __UpperCAmelCase			:				Optional[Any]													=    sum(primes[i:j]						)
               if sol >= ceiling:
                    break

               if sol in primes:
                    __UpperCAmelCase			:				Any													=    j - i
                    __UpperCAmelCase			:				List[Any]													=    sol

     return largest


if __name__ == "__main__":
      print(f"""{solution() = }""")