text: import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  
  # Improting the dataset
  dataset = pd.read_csv('Position_Salaries.csv')
  X = dataset.iloc[:, 1:2].values
  y = dataset.iloc[:, 2].values
  
  # Splitting the dataset into 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 = np.reshape(a=y, newshape=(-1, 1))
  y = sc_Y.fit_transform(y)
  
  
  
  # Fitting the SVR Model to the data set
  from sklearn.svm import SVR
  regressor = SVR(kernel = 'rbf')
  regressor.fit(X, y)
  
  #Predicting a new result
  
  # Following line didn't work for me
  # y_pred = sc_Y.inverse_transform(regressor.predict(sc_X.transform(np.array([6.5]))))
  
  y_pred = sc_Y.inverse_transform(regressor.predict(sc_X.transform(np.reshape(a=6.5, newshape=(1, -1)))))
  print(y_pred)
  
  # Visualizing the Regression results
  plt.scatter(X, y, color = 'red')
  plt.plot(X, regressor.predict(X), color = 'blue')
  plt.title('Truth or Bluff (SVR Model)')
  plt.xlabel('Position')
  plt.ylabel('salary')
  plt.show()
  
  """# Visualizing the Regression result in high resolution
  X_grid = np.arange(min(X), max(X), 0.1)
  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 (Regression Model)')
  plt.xlabel('Position')
  plt.ylabel('salary')
  plt.show()
  """
  