anchor.diff:  https://www.kaggle.com/c/titanic/submit (you need to create an account to
   submit your solution for evaluation) and submit your CSV file.
   
  -I got a score of xxx which you should get too since the algorithm we used is
  -deterministic (TO VERIFY).
  +I got a score of 0.79 which you should get too approximately.
   
   == Summary
   
  
positive.code:   .show()                // <2>
  ----
  <1> We filter the Dataset to obtain only the rows where the Fare column is null.
  <2> We want to display the resulting Dataset.
  
  As a result, we'll have to provide a default Fare value for this Dataset in
  addition to the one for the Age column. If we go back to our investigation of
  the Fare column, we had determined that the average fare for the training
  dataset was approximately 32.20$. We'll use this value in the row missing a Fare
  value:
  
  .Replacing null values in the Age and Fare column in the test Dataset
  [source,scala]
  ----
  val imputedTestMap = imputedTrainMap + ("Fare" -> 32.2d) // <1>
  
negative.code: ----
  <1> We define a map which contains the values we want to use for each column. In
  our case we want to replace null values in the Age column by 29.7.
  <2> na lets us access functions to work with missing data in our DataFrame.
  <3> We use the fill method in order to replace missing values according to our
  previously defined map.
  
  