A Data cleansing is correcting or removing incorrect, corrupted, distorted, duplicate, or incomplete information within a data set. When many data sources are combined, there are many occasions for data to be duplicated or mislabeled. If the data is wrong, the results and algorithms are unreliable, even if they appear correct. There is no absolute way to dictate the exact steps in the data cleansing process, as processes vary from one data set to another. But selecting a template for your data cleansing process is essential so you know you’re getting it right every time.
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How to Clean Data
Step 1 – Remove Duplicate or Irrelevant Observations
Remove unwanted observations from your dataset, including the exact or unrelated words. The same comments occur more frequently during data collection.
Step 2: Fix Structural Errors
Structural errors occur when you measure or transfer data and notice strange naming conventions, typos, or incorrect capitalization
Step 3 – Filter Out Unwanted Outliers
Often there are unique observations that, at first glance, don’t fit the data you’re designing. If you have a very legitimate reason to remove an outlier, e.g., incorrect data entry will help the performance of the data you are working with.
Step 4: Handle Missing Data
You cannot ignore missing data because many algorithms do not accept missing values. There are many ways to solve with missing data.
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