Single and Multiple Imputation Techniques to Treat Missing Numerical Variables (MNV) in Perspectives of Data Science Project - A Case Study

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Dharmendra Patel, Octavio Loyola-González, Arpit Trivedi, Hardik Rajgor, Tushar Mehta, Sanskruti Patel, Pranav Vyas, Nilay Ganatra, Hardik I Patel
DOI :  10.14445/22315381/IJETT-V70I5P202

Citation 

MLA Style: Dharmendra Patel, et al. "Single and Multiple Imputation Techniques to Treat Missing Numerical Variables (MNV) in Perspectives of Data Science Project - A Case Study." International Journal of Engineering Trends and Technology, vol. 70, no. 5, May. 2022, pp. 9-14. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P202

APA Style:Dharmendra Patel, Octavio Loyola-González, Arpit Trivedi, Hardik Rajgor, Tushar Mehta, Sanskruti Patel, Pranav Vyas, Nilay Ganatra, Hardik I Patel. (2022). Single and Multiple Imputation Techniques to Treat Missing Numerical Variables (MNV) in Perspectives of Data Science Project - A Case Study. International Journal of Engineering Trends and Technology, 70(5), 9-14. https://doi.org/10.14445/22315381/IJETT-V70I5P202

Abstract
Data Science is extensively used in various industrial domains to understand the enormous amount of data and derive meaningful and valuable insights to make smarter business decisions. The quality of data plays a vital role in insights generations. Data quality can be enhanced by imputing appropriate values in place of missing data. Data imputation plays a critical role in a data science project. In this paper, we have described single and multiple imputation techniques in the context of missing numerical variables with proper cases. We have explained different scenarios to select appropriate imputation techniques for any data science project. We also produce results based on imputation techniques by taking simple and meaningful examples.

Keywords
Single Imputation, Data Science, Numerical Variables, Missing Completely at Random(MCAR), Regression, Multiple Imputation.

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