# Getting data
import pandas as pd
df = pd.read_csv('Advertising.csv')

X = df[['TV']]
y = df['Sales']

# Create the train and test data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
                                                    random_state=42)

# Importing PredictionError visualizer
from yellowbrick.regressor import PredictionError

# Importing the Linear Regression model
from sklearn.linear_model import LinearRegression

# Creating the Prediction Error Plot
visualizer = PredictionError(LinearRegression(), bestfit=False)

visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)

# Saving plot in PNG format
visualizer.show(outpath="Prediction_Error_Plot.png")
	# Getting data
	import pandas as pd
	df = pd.read_csv('Advertising.csv')

	X = df[['TV']]
	y = df['Sales']

	# Create the train and test data
	from sklearn.model_selection import train_test_split
	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
	random_state=42)

	# Importing PredictionError visualizer
	from yellowbrick.regressor import PredictionError

	# Importing the Linear Regression model
	from sklearn.linear_model import LinearRegression

	# Creating the Prediction Error Plot
	visualizer = PredictionError(LinearRegression(), bestfit=False)

	visualizer.fit(X_train, y_train)
	visualizer.score(X_test, y_test)

	# Saving plot in PNG format
	visualizer.show(outpath="Prediction_Error_Plot.png")