code: # Simple Linear Regression
  
  # Importing the libraries
  import matplotlib.pyplot as plt
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  
  # Importing the dataset
  dataset = pd.read_csv('../datasets/Salary_Data.csv')
  X = dataset.iloc[:, :-1].values
  y = dataset.iloc[:, -1].values
  
  # Splitting the dataset into the Training set and Test set
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1.0/3.0, random_state = 0)
  
  # Fitting Simple Linear Regression to the Training set
  regressor = LinearRegression()
  regressor.fit(X_train, y_train)
  
  # Predicting the Test set results
  y_pred = regressor.predict(X_test)
  
  # Visualising the Training set results
  plt.scatter(X_train, y_train, color = 'red')
  plt.plot(X_train, regressor.predict(X_train), color = 'blue')
  plt.title('Salary vs Experience (Training set)')
  plt.xlabel('Years of Experience')
  plt.ylabel('Salary')
  plt.show()
  
  # Visualising the Test set results
  plt.scatter(X_test, y_test, color = 'red')
  plt.plot(X_train, regressor.predict(X_train), color = 'blue')
  plt.title('Salary vs Experience (Test set)')
  plt.xlabel('Years of Experience')
  plt.ylabel('Salary')
  plt.show()
  
apis: pandas.read_csv sklearn.linear_model.LinearRegression matplotlib.pyplot.xlabel matplotlib.pyplot.title matplotlib.pyplot.show matplotlib.pyplot.ylabel sklearn.model_selection.train_test_split matplotlib.pyplot.scatter