text: # Simple Linear Regression
  
  # Importing the libraries
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv('Salary_Data.csv')
  X = dataset.iloc[:, :-1].values
  y = dataset.iloc[:, 1].values
  
  # Splitting the dataset into the Training set and Test set
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)
  
  # Feature Scaling, not necessary as data is already preprocessed
  """
  from sklearn.preprocessing import StandardScaler
  sc_X = StandardScaler()
  X_train = sc_X.fit_transform(X_train)
  X_test = sc_X.transform(X_test)
  sc_y = StandardScaler()
  y_train = sc_y.fit_transform(y_train)
  """
  
  # Fitting Simple Linear Regression to the Training set
  from sklearn.linear_model import LinearRegression
  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()