




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_				:    Optional[int]										=     pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
a_				:    Union[str, Any]										=     dataset.iloc[:, 1:2].values
a_				:    str										=     dataset.iloc[:, 2].values


a_				:    Tuple										=     train_test_split(X, y, test_size=0.2, random_state=0)


a_				:    List[str]										=     PolynomialFeatures(degree=4)
a_				:    Tuple										=     poly_reg.fit_transform(X)
a_				:    Optional[Any]										=     LinearRegression()
pol_reg.fit(X_poly, y)





def 						UpperCAmelCase				(			)  ->     List[Any]:
			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









"""simple docstring"""
import math





def 						UpperCAmelCase				(			A__:    int = 100     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				sum(i * i for i in range(1							,				n + 1     )     )
			__lowerCamelCase					:	Union[str, Any]	       =				int(math.pow(sum(range(1							,				n + 1     )     )							,				2     )     )
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
					print(F"""{solution() = }""")



