text: import matplotlib.pyplot as plt
  import seaborn as sns
  from sklearn import linear_model
  from sklearn.datasets import make_regression
  from sklearn.model_selection import train_test_split
  
  # Create a data set for analysis
  x, y = make_regression(n_samples=500, n_features=1, noise=25, random_state=0)
  
  # Split the data set into testing and training data
  x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0)
  
  # Create a linear regression object
  regression = linear_model.LinearRegression()
  
  # Train the model using the training set
  regression.fit(x_train, y_train)
  
  # Make predictions using the testing set
  y_predictions = regression.predict(x_test)
  
  # Plot the data
  sns.set_style("darkgrid")
  sns.regplot(x_test, y_test, fit_reg=False)
  plt.plot(x_test, y_predictions, color='black')
  
  # Remove ticks from the plot
  plt.xticks([])
  plt.yticks([])
  
  plt.tight_layout()
  plt.show()
  