(2002) Statistical Analysis with Missing Data. Mathematical Modelling and Applications, 2020 5(2) 87-93. Comparative study of various methods of handling missing data. Available at:, Accessed on 21 December 2020. Journal of Official Statistics, 2003 19 153-176.Ĭhaitanya Baweja. Prevention and treatment of item nonresponse. International Journal of Public Opinion Research, 2010 22(4) 535-551.ĭe Leeuw E.D., Hox J.J. The relation between unit nonresponse and item non-response: A response continuum perspective. The prevention and handling of the missing data.
Furthermore, some recommendations to consider when dealing with missing data handling techniques were provided. This study tries to put these techniques and evaluation metrics in clear terms, followed by some mathematical equations. It lists some evaluation metrics used in measuring the performance of these techniques. This paper reviews some state-of-art practices obtained in the literature for handling missing data problems for machine learning. Since the accuracy and efficiency of machine learning models depend on the quality of the data used, there is a need for data analysts and researchers working with data, to seek out some relevant techniques that can be used to handle these inescapable missing values. Precisely, missing values are among the various challenges occurring in real-world data. Real-world data are commonly known to contain missing values, and consequently affect the performance of most machine learning algorithms adversely when employed on such datasets. Machine learning Data Missing Data Techniques Classification model Abstract Institute for Intelligent Systems, University of Johannesburg, Johannesburg, South Africa
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