Classification is a data science concept that involves assigning labels or categories to observations based on their attributes. This technique is commonly used for predictive modeling, where the goal is to create a model that can accurately predict the label or category of new observations. A simple example of classification is spam filtering in an email system, where the model classifies incoming emails as either spam or not spam based on their content.
Key Highlights
- Classification is a technique for assigning labels or categories to observations based on their attributes.
- It is commonly used in predictive modeling to create accurate models that can predict the label or category of new observations.
- A simple example of classification is spam filtering in an email system.
References
- Data Classification - Wikipedia
- Classification in Machine Learning - GeeksforGeeks
- What is Classification in Data Science? - Analytics Vidhya
Applying Classification in Business
Classification is a powerful technique that can be applied to a wide range of business problems, such as fraud detection, customer segmentation, and predictive maintenance. For example, a bank might use classification to detect fraudulent transactions by building a model that can accurately classify transactions as either legitimate or fraudulent based on their attributes. Similarly, an e-commerce company might use classification to segment its customers based on their buying behavior, allowing them to tailor their marketing campaigns to each segment. By using classification, businesses can make more informed decisions and gain valuable insights into their data.
