A Survey on Data Cleaning Methods for Improved Machine Learning Model Performance

TL;DR

This survey reviews recent advancements in data cleaning methods for machine learning, emphasizing their importance in improving data quality and model performance, and discusses future research directions.

Contribution

It provides a comprehensive overview of current data cleaning techniques, evaluates their effectiveness, and identifies gaps for future research in the field.

Findings

Recent methods improve data quality and model accuracy.

Effective frameworks are emerging for automated data cleaning.

Future research should focus on scalability and robustness.

Abstract

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.