A Probabilistic Approach for Missing Data Imputation

Author:

Arefin Muhammed Nazmul1ORCID,Masum Abdul Kadar Muhammad2ORCID

Affiliation:

1. Department of Computer Science and Engineering, International Islamic University Chittagong, Chattogram 4318, Bangladesh

2. Department of Software Engineering, Daffodil International University, Dhaka 1216, Savar, Bangladesh

Abstract

In the context of data analysis, missing data imputation is a vital issue due to the typically large scale and complexity of the datasets. It often results in a higher incidence of missing data. So, addressing missing data through the imputation technique is essential to ensure the integrity and completeness of the data. It will ultimately improve the accuracy and validity of the data analysis. The prime objective of this study is to propose an imputation model. This paper presents a method for imputing missing employee data through a combination of features and probability calculations. The study utilized employee datasets that were collected from the Kaggle along with primary data collected from RMG factories located in Chittagong. The suggested algorithm demonstrated a notable level of accuracy on the datasets, and the average accuracy for each identified technique was also quite satisfactory. This study contributes to the existing body of research on missing data imputation in big data analysis and offers practical implications for handling missing data in different datasets. Usage of this technique will enhance the accuracy of data analysis and decision-making in organizations.

Publisher

Hindawi Limited

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3