Empty values, also known as null values or missing values, are values that have no defined value in a data set. Empty values can arise for a number of reasons, such as missing information, data deletion or data corruption.
In machine learning, empty values can be a major problem because many machine learning algorithms cannot handle empty values. The presence of empty values can cause errors in data analysis and prediction. In addition, removing records containing empty values can significantly reduce the size of the dataset and affect the performance of the model.
There are different techniques for handling empty values in machine learning, such as removing records with empty values, imputing values, assigning default values, and modelling empty values as a separate feature. The choice of the appropriate technique will depend on the specific problem and the number and distribution of empty values in the dataset.
Normally the acronym NPLs (Non Performing Loans) is used in the financial sector and is a reality in Spanish banks as well as in banks [...].
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