




_lowerCamelCase      :  dict[str, float]      						=						{
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.6_0_9_3_4_4,
    "knot": 1.8_5_2,
}

_lowerCamelCase      :  dict[str, float]      						=						{
    "km/h": 1.0,
    "m/s": 0.2_7_7_7_7_7_7_7_8,
    "mph": 0.6_2_1_3_7_1_1_9_2,
    "knot": 0.5_3_9_9_5_6_8_0_3,
}




def 							_UpperCAmelCase  (UpperCamelCase_ : float      ,    UpperCamelCase_ : str      ,    UpperCamelCase_ : str       ):
    '''simple docstring'''
    if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
        _lowerCAmelCase    :    int										=      (
            F"Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"
            F"Valid values are: {', '.join(UpperCamelCase_       )}"
        )
        raise ValueError(UpperCamelCase_       )
    return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]      ,    3       )


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

