



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


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


UpperCAmelCase_		 =					PolynomialFeatures(degree=4)
UpperCAmelCase_		 =					poly_reg.fit_transform(X)
UpperCAmelCase_		 =					LinearRegression()
pol_reg.fit(X_poly, y)

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Union[str, Any]:
   plt.scatter(_snake_case	,   _snake_case	,   color='''red'''				)
   plt.plot(_snake_case	,   pol_reg.predict(poly_reg.fit_transform(_snake_case				)				)	,   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"""






def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				int:
  return 1 if input_a == input_a else 0




def 			snake_case		(		)						->				None:
  assert xnor_gate(0  ,						0	) == 1
  assert xnor_gate(0  ,						1	) == 0
  assert xnor_gate(1  ,						0	) == 0
  assert xnor_gate(1  ,						1	) == 1


if __name__ == "__main__":
     print(xnor_gate(0, 0))
     print(xnor_gate(0, 1))
     print(xnor_gate(1, 0))
     print(xnor_gate(1, 1))





