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Electrotechnical and Computer Engineering

Vol. 36 No. 07 (2021): Proceedings of the Faculty of Technical Sciences

STRATEGIES FOR DEALING WITH MISSING VALUES

  • Sreten Petrović
DOI:
https://doi.org/10.24867/13BE10Petrovic
Submitted
July 1, 2021
Published
2021-07-01

Abstract

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.