text: 
  # Importing the required libraries
  import pandas as pd
  import matplotlib.pyplot as plt
  import sklearn.cross_validation as cv
  
  
  # Importing the dataset from the data file
  datasetDS = pd.read_csv('CompSalary_Data.csv')
  #print (datasetDS)
  X = datasetDS.iloc[:, 0:7].values ## NOT  X = datasetDS.iloc[:, 0].values
  ## Since X is a Matrix here
  y = datasetDS.iloc[:, 1:2].values ## Can be  y = datasetDS.iloc[:, 1].values
  ## y is a Vector here
  #print (X)
  #print (y)
  
  # Visualize the Original dataset
  plt.scatter(X, y, color = 'blue')
  plt.plot(X, y, color='red')
  plt.title("Original Set's Experience vs Salary Regression Line")
  plt.xlabel("Years of Experience")
  plt.ylabel("Yearly Salary ($)")
  plt.grid()
  plt.show()
  
  # Splitting the data sets into Training and Test sets
  
  X_train, X_test = cv.train_test_split(X, test_size = .20, random_state = 0)
  
  y_train, y_test = cv.train_test_split(y, train_size = .80, random_state = 0)
  # Training  => 80% 
  
  # Fitting Simple Linear Regression to the Training set
  from sklearn.linear_model import LinearRegression
  regressor = LinearRegression()
  regressor.fit(X_train, y_train)
  
  #print (regressor)  ## Can't be printed as something visible
  
  # Predicting the Test set results
  y_pred = regressor.predict(X_test)
  #print (X_train)
  #print (y_train)
  
  print (y_pred)
  
  # Visualize the Training set results
  plt.scatter(X_train, y_train, color = 'blue')
  plt.plot(X_train, regressor.predict(X_train), color='red')
  plt.title("Training Set's Experience vs Salary Regression Line")
  plt.xlabel("Years of Experience")
  plt.ylabel("Yearly Salary ($)")
  plt.grid()
  plt.show()
  
  
  # Visualize the Test set results
  plt.scatter(X_test, y_test, color = 'blue')
  plt.plot(X_train, regressor.predict(X_train), color='red')
  plt.title("Test Set's Experience vs Salary Regression Line")
  plt.xlabel("Years of Experience")
  plt.ylabel("Yearly Salary ($)")
  plt.grid()
  plt.show()
  
  