A credit card company typically receives hundreds of thousands of applications for new cards. The application contains information regarding several different attributes, such as annual salary, any outstanding debts, age etc. The problem is to categorize applications into those who have good credit, bad credit, or fall into a gray area (thus requiring further human analysis). What pattern identification method in machine learning (classification or clustering) is applicable to this problem?

TYPES OF MACHINE LEARNING

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Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]

A	Classification
B	Clustering
C	Either A or B
D	None of the above

Explanation:

Detailed explanation-1: -’Classification’ is the machine learning technique that would be suggested to categorize applications into good credit and bad credit.

Detailed explanation-2: -Machine Learning technique that helps in answering the question and the group to which the data belongs to is Classification. * Classification in machine learning is a process of identifying to which of the set a group of experimental observations belongs.

Detailed explanation-3: -If you have a group of things, such as fruits or geometric shapes, you can classify them based on the property that they possess. For example, you can classify the apples in one category, the bananas in another, and so on. Similarly, geometric shapes can be classified as triangles, quadrilaterals, and so on.

There is 1 question to complete.