Feature transformations

In the previous two sections, we covered reading the train and test sets and combining them. We also handled some missing values. Now, we will use the random forest classifier of scikit-learn to predict the survival of passengers. Different implementations of the random forest algorithm accept different types of data. The scikit-learn implementation of random forest accepts only numeric data. So, we need to transform the categorical features into numerical ones.

There are two types of features:

• Quantitative: Quantitative features are measured in a numerical scale and can be meaningfully sorted. In the Titanic data samples, the Age feature is an example of a quantitative feature.

• Qualitative: Qualitative variables, also called categorical variables, are variables that are not numerical. They describe data that fits into categories. In the Titanic data samples, the Embarked (indicates the name of the departure port) feature is an example of a qualitative feature.

We can apply different kinds of transformations to different variables. The following are some approaches that one can use to transform qualitative/categorical features.