![]() ![]() The reduction in model accuracy, however, is actually the least of the problems that can occur when unclean data is used directly. Merging multiple datasets means that redundancies and duplicates are formed in the data, which then need to be removed.Īlso, incorrect and poorly collected datasets can often lead to models learning incorrect representations of the data, thereby reducing their decision-making powers. ![]() The importance of data cleaningĭata cleaning is a key step before any form of analysis can be made on it.ĭatasets in pipelines are often collected in small groups and merged before being fed into a model. This is generally data that can have a negative impact on the model or algorithm it is fed into by reinforcing a wrong notion.ĭata cleaning not only refers to removing chunks of unnecessary data, but it’s also often associated with fixing incorrect information within the train-validation-test dataset and reducing duplicates. Data cleaning is the process of preparing data for analysis by weeding out information that is irrelevant or incorrect. ![]()
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