Unstructured data is unfiltered information to which a fixed editing policy is not applied. It is often referred to as raw data. Common examples are internet logs, XML, JSON, text documents, images, videos and audio files. Unstructured data is searched and analyzed to extract useful facts. Up to 80% of enterprise data is unstructured. This means that it is the type of big data that is most visible to many people. The size of unstructured data requires scalable analytics to generate insights. Unstructured data is present in most, but not all, data lakes due to low storage costs.
Robotic Process Automation is defined as a technology that enables rule-based and repetitive business processes to be performed automatically by software robots.
Dirty data refers to data that is wrong for a company. This inaccuracy not only means that the data is not correct, the correct data can also be “dirty”.
Hyperparameter tuning is a technique used to optimize the performance of machine learning models. Hyperparameters are predetermined parameters that do not change throughout the learning process of the model. Correct selection of these parameters can significantly improve the accuracy of the model, the ability to generalize, and the computational efficiency.
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