| # 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 ResidualsPlot visualizer | |
| from yellowbrick.regressor import ResidualsPlot | |
| # Importing the Linear Regression model | |
| from sklearn.linear_model import LinearRegression | |
| # Creating the Residuals Plot | |
| visualizer = ResidualsPlot(LinearRegression(), hist=False) | |
| visualizer.fit(X_train, y_train) | |
| visualizer.score(X_test, y_test) | |
| # Saving plot in PNG format | |
| visualizer.show(outpath="Residuals_Plot.png") |