anchor.diff:  
   Let's start with the Pclass (passenger class) feature. From the description of
   the dataset, we expect 3 different values (1 to 3). Let's see if we're in the
  -right by looking at the unique values the Pclass column can take:
  +right by looking at the unique values the Pclass column can take (listing 2.6):
   
   .Checking the distinct values the Pclass column can take
   [source,scala]
  
positive.code: 
  This chapter covers:
  
  - The different concepts we saw in chapter 1 in action: Dataset, Transformer,
  Estimator and Pipeline
  - Understanding a small dataset in a machine learning context
  - Being confronted to a machine learning problem and solving it using Spark ML
  
  Now that we have a broad understanding of Apache Spark and its machine learning
  library Spark ML, we're ready to apply those newly-learned concepts to a
  
negative.code: distance between the Fare for a particular observation and the mean Fare
  (\~32$) is almost 50$ which indicates a skew towards higher fares with certainly
  quite a few outliers in the high price ranges influencing the standard deviation
  heavily.
  
  Even though we have identified a few problems with the Fare column we won't do
  much but it's always a good idea to deepen your understanding of a dataset
  before actually using it.
  
  We're left with the Age column, if we use the 'describe' method to get a better
  understanding of what's going on with this variable, we obtain table 2.3:
  
  .Results of calling describe on the Age column
  [options="header"]
  