724af6c3-722a-428b-a635-fb96a5824f6720250214041306984wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON COMPUTER RESEARCH2415-15211991-875510.37394/232018http://wseas.org/wseas/cms.action?id=13372110202511020251310.37394/232018.2025.13https://wseas.com/journals/cr/2025.phpRaedAlazaidahDepartment of Data Science and Artificial Intelligence, Faculty of Information Technology, Zarqa University, Zarqa, JORDANThe predictive performance of any classification or regression model highly depends on the quality of the collected data. Most of time datasets suffer from the problem of missing values, and hence, several techniques have been proposed to handle the problem of missing values. Consequently, this paper aims to quickly survey the most well-known techniques that handle missing data, and identify the best one to use concerning several issues such as the ratio of missing values, type of attributes in the dataset, number of instances, and number of class labels. 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