text: # Import pandas
  import pandas as pd
  # Use numpy to convert to arrays
  import numpy as np
  # Import csv
  import csv
  # To do square root on MSE
  from math import sqrt
  # To enables split (train and test) of the data
  from sklearn.model_selection import train_test_split
  # Import svm
  from sklearn import svm
  # Import scikit-learn metrics module for accuracy calculation
  from sklearn import metrics
  
  # Read the data for SVR
  df = pd.read_csv('D:/TIF/TIF SEM.7/Frontier Technology/Hotel Recommendations/GitHub Documentation/clean_train_one_percent.csv', sep=',')
  
  # Labels are the values we want to predict
  labels = np.array(df['hotel_cluster'])
  # Remove the labels from the features
  # axis 1 refers to the columns
  df = df.drop('hotel_cluster', axis=1)
  
  # Saving feature names for later use
  df_list = list(df.columns)
  # Convert to numpy array
  df = np.array(df)
  
  # Split the data into training and testing sets
  train_df, test_df, train_labels, test_labels = train_test_split(
      df, labels, test_size=0.25, random_state=50)
  
  # Create a svr regression
  # Radial Basis Function Kernel
  clf = svm.SVR(kernel='rbf', gamma='auto')
  
  # Train the model using the training sets
  clf.fit(train_df, train_labels)
  
  # Predict the response for test dataset
  pred = clf.predict(test_df)
  
  # Calculate the absolute errors
  errors = abs(pred - test_labels)
  
  # Print out the mean absolute error (MAE)
  print('MAE:', round(np.mean(errors), 2))
  # Print out the mean squared error (MSE)
  print('MSE: ', metrics.mean_squared_error(test_labels, pred))
  # Print out the root mean squared error (RMSE)
  print('RMSE: ', np.sqrt(metrics.mean_squared_error(test_labels, pred)))