Data in ML can be categorized into two types, (i)Quantitative or Numerical and (ii)Qualitative or Categorical.
Numerical Data: It is information about quantities, which means that it is information which can be measured, this data is represented as numbers and not words. Some examples of numerical data are height, age and shoe size.
Numerical data can be further divided into discrete and continuous. Continuous data are variables in the form of numbers that don't have a logical end to them, they can keep increasing without an end. Some examples are the height of children, the square footage of a two-bedroom house and speed of cars. Discrete data types are the opposite of continuous, they have a logical end to them. Some examples are no.of students in a class, no. of workers in a company, month and day.
Categorical Data: It is information that characterizes and approximates. This data type is non-numerical in nature. It can be a string of text or date.
Categorical data can be divided into ordinal, nominal and Binary. Ordinal data has some order to it. (Low, Medium, High) or (First, Second, Third) Nominal data is opposite to ordinal they have no order to it. (Gender, Hair colour) Binary data contains two values (0 and 1) or (yes and no).
That is it, folks. If anyone is interested in some advanced concept of ML head over to my git account, there are many tutorials which I have created keeping in mind the problems that novice data science enthusiast might face.
