Practical Applications of Classification Models

A classification model is a machine learning algorithm that tries to put input values into different pre-defined classes. For example, if you wanted a program to be able to automatically sort out the photos of you, your family members, and your pets, you would need to use a classification model. Different machine learning algorithms help to create different classification models

Can this technology be used in production and business? Absolutely. Continue reading this post for 3 great examples of CA used in business. 

Why is classification important?

Putting objects into categories allows one to better understand the nature of things and understand the research area. Systematically grouping observations into categories enables researchers and analysts to extract meaning from data. As biologists categorize plants, animals, and other lifeforms into different taxonomies, a classification model can do the same thing but with higher accuracy and at a higher speed in the fields of business, science, and healthcare. 

Enterprise software

How to sell more and make customers happy? Classification models help to break the audience into groups so that to provide each client with a more suitable offer to their lifestyles and needs. You can classify customers by the number of purchases, the frequency of visits to a website or shop, their purchasing habits. For example, letters from huge supermarket chains such as Target and Walmart work according to this system: each member of the loyalty program receives offers with discounts on the goods they most often buy. 


If before bank employees need to conduct huge research and be good phycologists to be sure that a borrower will repay the loan, now it is no longer necessary. 

Classification systems that are used by banks can process hundreds of different parameters in seconds. The results are based not only on the data of one particular borrower but also on hundreds of thousands of other clients. This way, classification systems help to determine the likelihood that the loan will be repaid based on the general portrait of the loan applicant rather accurately.

Medicine and healthcare

Medical diagnosis today heavily relies on machine learning. When we are talking about medicine, even the smallest deviations in the selected fragments can be harmful or dangerous: thickening, neoplasms, overestimated, or underestimated test values. The human factor error here is simply unforgivable because human lives can depend on it. A special algorithm that has been trained on thousands of results of other patients can notice even the slightest differences from the norm and alert the doctor. 


Classification models are used for high-quality content search, effective cross-selling, analysis of available customer information, automation of business processes, and many other purposes. The advantage of machine learning over human intelligence is that the machine can find hidden connections that can critically affect the outcome of the output. This advantage is very useful when processing big data, where it is difficult for a human specialist to find simple connections, not to mention when there are any hidden ones.