STRATEGIES FOR DEALING WITH MISSING VALUES
DOI:
https://doi.org/10.24867/13BE10PetrovicKeywords:
strategies, missing values, algorithms, machine learning, prediction, mobile applicationsAbstract
This paper deals with the presentation of strategies for dealing with missing values and showing their advantages, disadvantages and efficiency in combination with machine learning algorithms while predicting the popularity of mobile applications.
References
[1] D. B. Rubin, “Inference and missing data”, Biometrika 1976.
[2] https://www.kaggle.com/parulpandey/a-guide-to-handling-missing-values-in-python (pristupljeno u septembru 2020.)
[3] Z. Zhang, “Missing data imputation: focusing on single imputation”, 2016.
[4] H. Kang, “The prevention and handling of the missing data”, 2013.
[5] J. Zhang, D. Chen, “Interpolation calculation made EZ”
[6] Jasmina Đ. Novaković, “Rešavanje klasifikacionih problema mašinskog učenja”, 2013.
[7] G. Lee, T. S. Raghu; “Determinants of Mobile Apps Success: Evidence from App Store”, 2014.
[2] https://www.kaggle.com/parulpandey/a-guide-to-handling-missing-values-in-python (pristupljeno u septembru 2020.)
[3] Z. Zhang, “Missing data imputation: focusing on single imputation”, 2016.
[4] H. Kang, “The prevention and handling of the missing data”, 2013.
[5] J. Zhang, D. Chen, “Interpolation calculation made EZ”
[6] Jasmina Đ. Novaković, “Rešavanje klasifikacionih problema mašinskog učenja”, 2013.
[7] G. Lee, T. S. Raghu; “Determinants of Mobile Apps Success: Evidence from App Store”, 2014.
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Published
2021-07-01
Issue
Section
Electrotechnical and Computer Engineering
