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Veracity in big data analytics is recognized as a complex issue in data preparation process,
\ninvolving imperfection, imprecision and inconsistency. Single-valued Neutrosophic numbers
\n(SVNs), have prodded a strong capacity to model such complex information. Many Data mining
\nand big data techniques have been proposed to deal with these kind of dirty data in preprocessing
\nstage. However, only few studies treat the imprecise and inconsistent information inherent in the
\nmodeling stage. However, this paper summarizes all works done about mapping machine learning
\nalgorithms from crisp number space to Neutrosophic environment. We discuss also contributions
\nand hybridization of machine learning algorithms with Single-valued Neutrosophic numbers
\n(SVNs) in modeling imperfect information, and then their impacts on resolving reel world problems.
\nIn addition, we identify new trends for future research, then we introduce, for the first time,
\na taxonomy of Neutrosophic learning algorithms, clarifying what algorithms are already processed
\nor not, which makes it easier for domain researchers.