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


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


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


def 							__A()		->				List[str]:

		"""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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__	=							{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							[
		    "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MCTCTForCTC",
		    "MCTCTModel",
		    "MCTCTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
		from .feature_extraction_mctct import MCTCTFeatureExtractor
		from .processing_mctct import MCTCTProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
		import sys

		lowerCamelCase__	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



