Data Cleaning, Munging, Wrangling, Janitor#
Data cleaning is the process of identifying and correcting or removing incomplete, incorrect, irrelevant, or duplicate data from a dataset. The goal of data cleaning is to improve the quality and accuracy of a dataset, and make it more suitable for analysis and use in machine learning and other applications. Data cleaning typically involves a combination of manual and automated processes, and can be an important step in the overall process of data preparation.