Data Cleaning

Data Cleaning, also known as data cleansing or data scrubbing, is the process of identifying and correcting or removing inaccurate, incomplete, or irrelevant data from a dataset. The goal of data cleaning is to improve the quality of data for analysis and decision-making. This involves various tasks, including correcting typographical errors, standardizing formats, removing duplicates, handling missing values, and ensuring consistency across different datasets. By enhancing the reliability and usability of data, data cleaning helps organizations make informed decisions, improve operational efficiency, and maintain data integrity. This process is essential in data management, analytics, and machine learning, as high-quality data is crucial for generating accurate insights and predictions.