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Visualizing a linear regression model

Now you have built your linear regression model and trained it using all available observations, you can visualize how well the model fits the data. This allows you to interpret the relationship between radio advertising expenditure and sales values.

The variables X, an array of radio values, y, an array of sales values, and predictions, an array of the model's predicted values for y given X, have all been preloaded for you from the previous exercise.

Questo esercizio fa parte del corso

Supervised Learning with scikit-learn

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Istruzioni dell'esercizio

  • Import matplotlib.pyplot as plt.
  • Create a scatter plot visualizing y against X, with observations in blue.
  • Draw a red line plot displaying the predictions against X.
  • Display the plot.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import matplotlib.pyplot
import ____.____ as ____

# Create scatter plot
plt.scatter(____, ____, color="____")

# Create line plot
plt.plot(____, ____, color="____")
plt.xlabel("Radio Expenditure ($)")
plt.ylabel("Sales ($)")

# Display the plot
plt.____()
Modifica ed esegui il codice