Attributes and its types in data analytics

In this article, we are going to discuss attributes and it’s various types in data analytics. We will also cover attributes types with the help of example for better understanding. So let’s discuss one by one.

Attributes :
An attribute is a data item that appears as a property of a data entity. Machine learning literature tends to use the term feature while statisticians prefer the term variable.
Example –
Let’s consider an example like name, address, email, etc. are the attributes for the contact information.

Perceived values for a given attribute are termed as observations. The variety of an attribute is insisted on by the set of feasible values – nominal, binary, ordinal, or numeric.

Types of Attributes :

  • Nominal Attributes :
    Nominal means “relating to names” . The utilities of a nominal attribute are sign or title of objects . Each value represents some kind of category, code or state, and so nominal attributes are also referred to as categorial.



    Example –
    Suppose that skin color and education status are two attributes of expressing person objects. In our implementation, possible values for skin color are dark, white, brown. The attributes for education status can contain the values- undergraduate, postgraduate, matriculate. Both skin color and education status are nominal attributes.

  • Binary Attributes :
    A binary attribute is a category of nominal attributes that contains only two classes: 0 or 1, where 0 often tells that the attribute is not present, and 1 tells that it is existing. Binary attributes are mentioned as Boolean if the two conditions agree to true and false.

    Example –
    Given the attribute drinker narrate a patient item, 1 specify that the drinker drinks, while 0 specify that the patient does not. Similarly, suppose the patient undergoes a medical test that has two practicable outcomes.

  • Ordinal Attributes :
    An ordinal attribute is an attribute with a viable advantage that has a significant sequence or ranking among them, but the enormity between consecutive values is not known.

    Example –
    Suppose that food quantity corresponds to the variety of dishes available at a restaurant. The nominal attribute has three possible values: starters, main course, combo.

    The values have a meaningful sequence that corresponds to different food quantity however, we cannot tell from the values how much bigger, say, a medium is than a large.

  • Numeric Attributes :
    A numeric attribute is calculable, that is, it is a quantifiable amount that constitutes integer or real values.
    Numeric attributes can be of two types as follows: Interval- scaled, and Ratio – scaled.

    Let’s discuss one by one.

    1. Interval – Scaled Attributes :
      Interval – scaled attributes are calculated on a lamella of uniform- size units. The values of interval-scaled attributes have order and can be positive, 0, or negative. Thus, in addition to providing a ranking of values, such attributes allow us to compare and quantify the difference between values.

      Example –
      A temperature attribute is an interval – scaled. We have different temperature values for every new day, where each day is an entity. By sequencing the values, we obtain an arrangement of entities with reference to temperature. In addition, we can quantify the difference in the value between values, for example, a temperature of 20 degrees C is five degrees higher than a temperature of 15 degrees C.

    2. Ratio – Scaled Attributes :
      A ratio – scaled attribute is a category of a numeric attribute with imminent or fix zero points. In inclusion, the entities are structured, and we can also compute the difference between values, as well as the mean, median, and mode.

      Example –
      The Kelvin (K) temperature scale has what is contemplated as a true zero point. It is the point at which the tiny bits that consist of matter has zero kinetic energy.

  • Discrete Attribute :
    A discrete attribute has a limited or restricted unlimited set of values, which may appear as integers. The attributes skin color, drinker, medical report, and drink size each have a finite number of values, and so are discrete.

  • Continuous Attribute :
    A continuous attribute has real numbers as attribute values.

    Example –
    Height, weight, and temperature have real values . Real values can only be represented and measured using finite number of digits . Continuous attributes are typically represented as floating-point variables.

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