anchor.diff:  
   * maximize the number of explanatory variables: those that are correlated with the outcome variable, and 
   
  -* compensate for the correlation of explanatory variables to each other (link:https://en.wikipedia.org/wiki/Multicollinearity[multicollinearity]).
  +* compensate for the correlation of explanatory variables to each other (link:https://en.wikipedia.org/wiki/Multicollinearity[multicollinearity^]).
   
   In other words, we need to find the fewest quantity of variables that can explain almost everything that is going on with the outcome that we want to predict.
   |===
  
positive.code: :hp-alt-title: Predict Survival Propensity of Titanic Passengers
  :hp-tags: Blog, Open_Source, Machine_Learning, Analytics, Data_Science
  :icons: image
  :toc: macro 
  
  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!
  
  toc::[]
  
  
negative.code: 
  * learn to reason and problem solve like a data scientist
  * get somewhat comfortable with RStudio
  * predict whether a passenger would survive the sinking of the link:https://en.wikipedia.org/wiki/RMS_Titanic[Titanic^]
  * enter a Kaggle submission file for evaluation
  * have fun!
  
  