Overview of data cleaning methods for machine learning
Abstract
In the last few years, machine learning models and neural networks have been actively introduced into everyday life. The main parameters in their training are accuracy and efficiency. One of the main steps that allows you to improve these indicators is to prepare a data set. Before applying any method, it is necessary to perform a preliminary cleaning of the data, since otherwise the results obtained may be inaccurate or incorrect. Even though novice researchers prepare data sets, cleaning is often performed incorrectly or inefficiently with lots of errors. This article provides an overview of the main methods, considers their advantages and disadvantages, and gives general recommendations to improve the data cleaning process. In addition, special attention is paid to the importance of the ability to use various tools for data cleaning. The main libraries such as Pandas, scikit-learn, and NumPy, specialized programs such as OpenRefine, various features of the R language, as well as methods of normalization, standardization, and processing of text data are considered. The correct use of data cleaning tools significantly affects the quality of analysis and modeling, contributing to more accurate and reliable results.
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