What is Data Cleaning?

Data cleaning, also known as data cleansing, is the process of identifying and correcting (or removing) inaccurate, incomplete, irrelevant, redundant, or inconsistent data within a dataset. It is a crucial step in data preprocessing to ensure the quality and reliability of data used for analysis, reporting, and decision-making. Common tasks include handling missing values, correcting typos, standardizing formats, removing duplicates, and resolving inconsistencies between different data sources. Data cleaning improves the accuracy and consistency of data, leading to better insights and more reliable results.