Data cleaning is the process of detecting and correcting errors and inconsistencies in a dataset to improve its quality and reliability for analysis. This involves identifying missing or incorrect values, removing duplicates, standardizing formats, and resolving inconsistencies in data entries. Data cleaning is essential to ensure that the data used for analysis is accurate and valid, and can involve a combination of automated tools and manual processes. It is a crucial step in the data preprocessing phase of data analysis and machine learning projects.