





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


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


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



def        __lowercase       (				)			->       Any:


      '''simple docstring'''


      plt.scatter(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						color="""red"""     )
      plt.plot(_SCREAMING_SNAKE_CASE			,						pol_reg.predict(poly_reg.fit_transform(_SCREAMING_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









from __future__ import annotations



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       list[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						2
      SCREAMING_SNAKE_CASE		=						[]
      while i * i <= n:
            if n % i:
                  i += 1
            else:
                  n //= i
                  factors.append(_SCREAMING_SNAKE_CASE     )
      if n > 1:
            factors.append(_SCREAMING_SNAKE_CASE     )
      return factors


if __name__ == "__main__":
      import doctest

      doctest.testmod()


