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


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


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



def 						lowercase_      (       )							->							str:
    """simple docstring"""

    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



from ..utils import DummyObject, requires_backends







class 					lowerCamelCase		(  metaclass=A_  ):
     UpperCAmelCase__    :							Union[str, Any]									=		["onnx"]





     def __init__(self				:   Tuple    ,   *_A				:   Optional[int]    ,   **_A				:   Any       )						->						Dict:
         requires_backends(self    ,   ["onnx"]       )





     @classmethod
     def 		UpperCAmelCase(cls				:   int    ,   *_A				:   Dict    ,   **_A				:   List[Any]       )						->						Optional[Any]:
         requires_backends(cls    ,   ["onnx"]       )





     @classmethod
     def 		UpperCAmelCase(cls				:   Dict    ,   *_A				:   Tuple    ,   **_A				:   Optional[Any]       )						->						int:
         requires_backends(cls    ,   ["onnx"]       )

