Working with categorical and textual data

Typically, you'll find yourself dealing with two main kinds of data: categorical and numerical. Numerical data, such as temperature, amount of money, days of usage, or house number, can be composed of either floating point numbers (like 1.0, -2.3, 99.99, …) or integers (like -3, 9, 0, 1, …). Each value that the data can assume has a direct relation with others since they're comparable. In other words, you can say that a feature with a value of 2.0 is greater (actually, it is double) than a feature that assumes a value of 1.0. This type of data is very well-defined and comprehensible, with binary operators such as equal, greater, and less.

The other type of data you might see in your career is the categorical type. A categorical datum expresses an attribute that cannot be measured and assumes values in a finite or infinite set of values, often named levels. For example, weather is a categorical feature since it takes values in the discrete set [sunny...