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.

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关于改进机器学习模型性能的数据清理方法的调查

数据清理是任何机器学习项目的初始阶段，也是数据分析中最关键的过程之一。这是确保数据集没有不正确或错误数据的关键步骤。它可以使用数据整理工具手动完成，也可以使用计算机程序自动完成。数据清理需要一系列程序，一旦完成，就可以为分析准备数据。鉴于其在众多领域的重要性，人们对开发高效的数据清理框架越来越感兴趣。在本次调查中，研究了数据清理方法的一些最新进展，以了解其有效性，并建议未来的研究方向以缩小每种方法的差距。