anchor.diff:  
   After training and validating our predictive model(s), we can then enter the submission file to Kaggle for evaluation. As we iterate, we can submit more files and assess our progress on the leaderboard. Subtle model improvements can lead to significant leaps on the leaderboard.
   
  -//[icon="/images/note.png"]
  +:icons:
  +
  +[icon="/images/note.png"]
   .*Data Science Perspective*
   [TIP]
   =====================================
  
positive.code: = Titanic: Learning Data Science with RStudio
  :hp-alt-title: Predict Survival Propensity of Titanic Passengers
  :hp-tags: Blog, Open_Source, Machine_Learning, Analytics, Data_Science
  
  So we are aspiring data scientists and want to dip our toes into link:http://rmarkdown.rstudio.com/[RStudio]. How do we get started? We dive into the into the waters of the link:https://www.kaggle.com/c/titanic[Kaggle Titanic "Competition"], of course!
  
  
negative.code: 
  In other words, we need to find the fewest quantity of variables that can explain everything that is going on with the outcome that we want to predict.
  =====================================
  
  :icons: font
  
  == Titanic History Lesson
  
  