




_lowerCamelCase      :  str      						=						{
    0: "0",
    1: "1",
    2: "2",
    3: "3",
    4: "4",
    5: "5",
    6: "6",
    7: "7",
    8: "8",
    9: "9",
    1_0: "a",
    1_1: "b",
    1_2: "c",
    1_3: "d",
    1_4: "e",
    1_5: "f",
}




def 							_UpperCAmelCase  (UpperCamelCase_ : float       ):
    '''simple docstring'''
    assert type(UpperCamelCase_       ) in (int, float) and decimal == int(UpperCamelCase_       )
    _lowerCAmelCase    :    int										=      int(UpperCamelCase_       )
    _lowerCAmelCase    :    int										=      ''''''
    _lowerCAmelCase    :    Dict										=      False
    if decimal < 0:
        _lowerCAmelCase    :    int										=      True
        decimal *= -1
    while decimal > 0:
        _lowerCAmelCase    :    List[Any]										=      divmod(UpperCamelCase_      ,    16       )
        _lowerCAmelCase    :    List[str]										=      values[remainder] + hexadecimal
    _lowerCAmelCase    :    Tuple										=      '''0x''' + hexadecimal
    if negative:
        _lowerCAmelCase    :    List[Any]										=      '''-''' + hexadecimal
    return hexadecimal


if __name__ == "__main__":
 import doctest

 doctest.testmod()







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


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


_lowerCamelCase      :  Optional[Any]      						=						PolynomialFeatures(degree=4)
_lowerCamelCase      :  Optional[Any]      						=						poly_reg.fit_transform(X)
_lowerCamelCase      :  Dict      						=						LinearRegression()
pol_reg.fit(X_poly, y)




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				plt.scatter(UpperCamelCase_      ,    UpperCamelCase_      ,    color="""red"""       )
				plt.plot(UpperCamelCase_      ,    pol_reg.predict(poly_reg.fit_transform(UpperCamelCase_       )       )      ,    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

