adatyamítást
Adatyamítást, often translated as data enrichment or data augmentation, is the process of adding supplementary information to existing data. This can involve combining data from multiple sources, deriving new attributes from existing ones, or using external datasets to add context. The goal is to improve the quality, completeness, and utility of the original data, making it more valuable for analysis, decision-making, or machine learning model training.
There are several methods for adatyamítást. One common approach is to link datasets based on common identifiers,
The benefits of adatyamítást are numerous. Enriched data can lead to more accurate insights and predictions,