text: # SVR
  
  # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv('Position_Salaries.csv')
  X = dataset.iloc[:, 1:2].values
  y = dataset.iloc[:, 2].values
  
  # Splitting the dataset into the Training set and Test set
  """from sklearn.cross_validation import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"""
  
  # Feature Scaling
  from sklearn.preprocessing import StandardScaler
  sc_X = StandardScaler()
  sc_y = StandardScaler()
  X = sc_X.fit_transform(X)
  y = sc_y.fit_transform(y)
  
  # Fitting SVR to the dataset
  from sklearn.svm import SVR
  regressor = SVR(kernel = 'rbf')
  regressor.fit(X, y)
  
  # Predicting a new result
  y_pred = regressor.predict(sc_X.transform(np.array([[6.5]])))
  y_pred = sc_y.inverse_transform(y_pred)
  
  # Visualising the SVR results
  plt.scatter(X, y, color = 'red')
  plt.plot(X, regressor.predict(X), color = 'blue')
  plt.title('Truth or Bluff (SVR)')
  plt.xlabel('Position level')
  plt.ylabel('Salary')
  plt.show()
  
  # Visualising the SVR results (for higher resolution and smoother curve)
  X_grid = np.arange(min(X), max(X), 0.01) # choice of 0.01 instead of 0.1 step because the data is feature scaled
  X_grid = X_grid.reshape((len(X_grid), 1))
  plt.scatter(X, y, color = 'red')
  plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
  plt.title('Truth or Bluff (SVR)')
  plt.xlabel('Position level')
  plt.ylabel('Salary')
  plt.show()