new_contents: from sklearn.svm import SVC
  
  
  def SVMI(data, column, missing_values='NaN'):
      # train_data = DataFrame with data[column] different than missing values
      # test_data = DataFrame with data[column] equal than missing values
      # set X, and y from train_data
      # drop column from test_data
      # fit and predict
  
      pass
  
subject: Add preprocessing draft