Classification Problems Real-life Examples

classification problems real life examples

In this post, you will learn about some popular and most common real-life examples of machine learning classification problems. For beginner data scientists, these examples will prove to be helpful to gain perspectives on real-world problems which can be termed as machine learning classification problems. This post will be updated from time-to-time to include interesting real-life examples which can be solved by training machine learning classification models.

Before going ahead and looking into examples, let’s understand a little about what is machine learning (ML) classification problem. You may as well skip this section if you are familiar with the definition of machine learning classification problems & solutions. 

What are ML Classification Problems?

Machine learning classification problems are those which requires the given data set to be classified in two or more categories. For example, whether a person is suffering from a disease X (answer in Yes or No) can be termed as classification problem.

Classification problems can be of the following different types:

  • Binary classification – Classifies data into two classes such as Yes / No, good / bad, high / low, suffers from a particular disease or not etc
  • Multinomial classification: Classifies data into three or more classes; Document classification, product catgeorization, malware classification

Classification problems are supervised learning problems wherein the training data set consists of data related to independent variables and response variable (label). The classification models are trained using some of the following algorithms:

  • Logistic regression
  • Decision trees
  • Random forest
  • XGBoost
  • Light GBM
  • Voting classifiers

Classification Problems Real-world examples

Here is the list of real-life examples of machine learning classification problems:

  • Customer behavior prediction: Customers can be classified in different categories based on their buying patterns, web store browsing patterns etc.
  • Web text prediction: Classifies web text or assign tag to web text based on pre-determined categories learned from the past data
  • Ad click through rate prediction – Binary classification – Whether one or more ads on the website will be clicked or not
  • Product categorization – Multinomial classification – Categorize the products sold by different retailers in same categories irrespective of categories assigned to the product by the respective retailers. This use case is relevant for ecommerce aggregators. Read this page on product categorization for greater details.
  • Malware classification – Multinomial classification – Classify the new / emerging malwares on the basis of comparable features of similar malwares
  • Customer churn prediction – Binary classification – Whether a customer will churn or not in near future
  • Deduction validation classification – Binary classification – Whether a deduction claimed by the buyer on a given invoice is valid or invalid deduction
  • Blocked order release recommendation – Binary classification – Classifies whether an order placed by customer should be blocked or not based on the buyer credit exposure
  • Document classification – Multinomial classification – Classifies documents in different categories
Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin.
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