Classification datasets is a process of inspecting, cleaning, transforming data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Classification datasets has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

Data mining is a particular classification datasets technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers classification datasets that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide it into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data.

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