


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


_SCREAMING_SNAKE_CASE			: Dict			      =    train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE			: Optional[Any]			      =    PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE			: str			      =    poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE			: Optional[int]			      =    LinearRegression()
pol_reg.fit(X_poly, y)
def 			UpperCAmelCase__						():



     """simple docstring"""






     plt.scatter(lowerCamelCase_							,lowerCamelCase_							,color='''red'''				)
     plt.plot(lowerCamelCase_							,pol_reg.predict(poly_reg.fit_transform(lowerCamelCase_				)				)							,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 .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




