Analytical Approach of the Allegation of Nonexistent and Deficient Knowledge Using Machine Learning Techniques

Abstract

Missing data is a problem that infuses most important issue faced by researchers and practitioners who use
industrial and research databases is incompleteness of data, usually in terms of missing or erroneous values. Some of the
data analysis algorithms can work with incomplete data, a large portion of work require complete data. Therefore, variety of
machine learning (ML) techniques are developed to reprocess the incomplete data. This paper concentrates on different
imputation techniques and also proposes supervised and unsupervised machine learning techniques Naïve Bayesian
imputation method in MI model. The analysis is carried out using a comprehensive range of databases, for which missing
values were introduced randomly. The goal of this paper is to provide general guidelines on selection of suitable data
imputation algorithms based on characteristics of the data.