Data cleaning, also known as data cleansing or data scrubbing, is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It involves detecting and handling missing values, correcting typos and spelling errors, dealing with duplicate records, resolving inconsistencies in formatting or coding, and addressing outliers or anomalies in the data.
Data cleaning is an essential step in the data preprocessing phase of any data analysis or machine learning project. It helps ensure that the data is accurate, reliable, and suitable for analysis or modeling. By cleaning the data, researchers and analysts can improve the quality and integrity of their findings and avoid drawing incorrect conclusions based on flawed or incomplete data.
Common techniques used in data cleaning include data imputation (filling in missing values), deduplication (removing duplicate records), standardization (converting data to a consistent format), outlier detection (identifying and handling extreme values), and validation (verifying data integrity and correctness).
Overall, data cleaning is a crucial step in the data management process, as it helps to enhance the quality and reliability of the data, leading to more accurate and meaningful insights.
Click on the link below to read more about data cleaning and how to clean data online.
https://bigpro1.com/en/what-is-data-cleaning/
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