Exploring the Best Imputation Technique for Handling Missing Data : A Review and Comparative Analysis of Methods

Authors

  • Dr. Rashmi Dahra  Department of Computer Applications, GVM Institute of Technology and Management, Sonepat, Haryana, India
  • Dr. Manju Papreja  Department of Computer Applications, GVM Institute of Technology and Management, Sonepat, Haryana, India
  • Dr. Renu Kakkar  Department of Computer Applications, GVM Institute of Technology and Management, Sonepat, Haryana, India

Keywords:

Imputation, Missingness, MCAR, MAR, MNAR

Abstract

In the world of Statistics and analysis, missing data is a pertinent problem. and has significant impact on data-driven projects, as machine learning models rely heavily on high-quality data to produce accurate solutions to real-world problems. This paper explains comprehensive comparison of different imputation (replacing missing data with estimated data) techniques predicated on several factors such as type of data, distribution of variables, amount and pattern of missing data, and. There are multiple methods of imputation including single imputation, multiple imputation, hot deck imputation, machine learning-based imputation, and listwise deletion. The advantages and disadvantages of each technique are also discussed, along with their assumptions and software availability. This paper aims to provide a practical guide for researchers and practitioners in selecting the appropriate imputation technique for their data based on its characteristics and research question.

References

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International Journal of Scientific Research in Science and Technology

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Published

2023-12-30

Issue

Volume 10, Issue 6, November-December-2023

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. Rashmi Dahra, Dr. Manju Papreja, Dr. Renu Kakkar, " Exploring the Best Imputation Technique for Handling Missing Data : A Review and Comparative Analysis of Methods, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 10, Issue 6, pp.135-143, November-December-2023.