Description: Automated data cleaning refers to the use of algorithms and software tools to automatically detect and correct errors in data. This process is fundamental in data preprocessing, as it ensures that the information used in analysis and machine learning models is accurate and reliable. Data cleaning can include removing duplicates, correcting typographical errors, normalizing formats, and managing missing values. The main features of this technique include its ability to efficiently handle large volumes of data, its speed compared to manual cleaning, and its potential to improve data quality, which in turn optimizes the results of subsequent analyses. The relevance of automated data cleaning lies in its ability to reduce the time and effort required to prepare data, allowing analysts and data scientists to focus on interpreting and using the information rather than preparing it. In a world where data is becoming increasingly abundant, automated data cleaning has become an essential tool for ensuring the integrity and usefulness of data in various applications, from scientific research to business decision-making and marketing.
