text: # Support Vector Regression (SVR)
  
  # Importing the libraries
  import numpy as np
  # no visual graph in this template, plt is commented out as such
  # import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv('Data.csv')
  X = dataset.iloc[:, :-1].values
  y = dataset.iloc[:, -1].values
  y = y.reshape(len(y),1)
  
  # 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 = 0.2, random_state = 0)
  
  # Feature Scaling
  from sklearn.preprocessing import StandardScaler
  sc_X = StandardScaler()
  sc_y = StandardScaler()
  X_train = sc_X.fit_transform(X_train)
  y_train = sc_y.fit_transform(y_train)
  
  # Training the SVR model on the Training set
  from sklearn.svm import SVR
  regressor = SVR(kernel = 'rbf')
  regressor.fit(X_train, y_train)
  
  # Predicting the Test set results
  y_pred = sc_y.inverse_transform(regressor.predict(sc_X.transform(X_test)))
  np.set_printoptions(precision=2)
  print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))
  
  # Evaluating the Model Performance
  from sklearn.metrics import r2_score
  r2_score(y_test, y_pred)