Data Science is an art that benefits from a human element. Enter feature engineering: creatively engineering your own features by combining the different existing variables.
While feature engineering is a discipline in itself, too broad to be covered here in detail, you will have a look at a simple example by creating your own new predictive attribute: family_size.
A valid assumption is that larger families need more time to get together on a sinking ship, and hence have lower probability of surviving. Family size is determined by the variables SibSp andParch, which indicate the number of family members a certain passenger is traveling with. So when doing feature engineering, you add a new variable family_size, which is the sum of SibSpand Parch plus one (the observation itself), to the test and train set.

What techniques can you use to improve your predictions even more? One possible way is by making use of the machine learning method Random Forest. Namely, a forest is just a collection of trees…