Description: Gap Filling is a fundamental process in data preprocessing used to address the issue of missing values in a dataset. This phenomenon is common in various fields, such as scientific research, data analytics, and machine learning, where the absence of information can compromise the quality and accuracy of analyses. The goal of gap filling is to replace these missing values with estimates that maintain the integrity of the dataset. There are various techniques to carry out this process, ranging from simple methods, such as mean or median imputation, to more complex approaches, such as multiple imputation or the use of machine learning algorithms. The choice of the appropriate method depends on the type of data, the amount of missing values, and the context of the analysis. Gap filling not only improves data quality but also enables more robust and accurate analyses, facilitating informed decision-making across multiple domains. In summary, gap filling is an essential technique in data preprocessing aimed at optimizing the quality of datasets by addressing the issue of missing values.
