  Data cleaning is the initial stage of any machine learning project and is one
of the most critical processes in data analysis. It is a critical step in
ensuring that the dataset is devoid of incorrect or erroneous data. It can be
done manually with data wrangling tools, or it can be completed automatically
with a computer program. Data cleaning entails a slew of procedures that, once
done, make the data ready for analysis. Given its significance in numerous
fields, there is a growing interest in the development of efficient and
effective data cleaning frameworks. In this survey, some of the most recent
advancements of data cleaning approaches are examined for their effectiveness
and the future research directions are suggested to close the gap in each of
the methods.
