text: # ###########################################################################################################################
  # Terry McCann. DevOps for Data Science. 
  # Example Python model. 
  # 2018
  # ###########################################################################################################################
  
  # Import the required libaraies 
  import numpy as np
  from sklearn import datasets, linear_model
  
  # Load in the datasets from SKlearn. We will be using the diabetes dataset. 
  diabetes = datasets.load_diabetes()
  
  # Use only one feature. To make the prediction simple. 
  diabetes_X = diabetes.data[:, np.newaxis, 2]
  
  # Split the data into training/testing sets
  diabetes_X_train = diabetes_X[:-20]
  diabetes_X_test = diabetes_X[-20:]
  
  # Split the targets into training/testing sets
  diabetes_y_train = diabetes.target[:-20]
  diabetes_y_test = diabetes.target[-20:]
  
  # Create linear regression object
  regr = linear_model.LinearRegression()
  
  # Train the model using the training sets
  regr.fit(diabetes_X_train, diabetes_y_train)
  
  # Make predictions using the testing set
  diabetes_y_pred = regr.predict(diabetes_X_test)
  
  # Examples to show. 
  #print(diabetes_y_pred)
  
  NewValue = 12
  NewValuePrediction = regr.predict(NewValue)
  
  print(NewValuePrediction)
  