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data cleaning data cleansing or data cleaning is the process of identifying enrollment or correcting inaccurates records from a data set and refer to recognizing unfinished unreliable inaccurates or non-relevant parts of the data and then restoring remodeling or removing the dirty or crude data remove duplicates record duplicate observations will happen most often during data collection when you combine data sets from multiple places scrap data or receive data from clients or multiple departments there are possibilities to create duplicate data structural error structural error or when you measure or transfer data and notice strange naming conventions types or incorrect type capitalizations these inconsistencies can cause mislabel categories handling missing data missing data also known as missing values is where some of the observation in our data set are blank you canamp;#39;t ignore missing data because many algorithms will not accept missing values there are a couple of ways to de
