Cleaning data means properly fixing, removing, or correcting data that may be incorrect, corrupted, incorrectly formatted, duplicated, or incomplete. The steps in the data cleaning process will vary from dataset to dataset, so there is no one method that prescribes the exact steps. Still, it is critical to establish a framework to cover some basic best practices.
Step 1: Eliminate duplicates
The duplication of data can happen during data collection and cause many problems in your dataset. Therefore, you must create a process to monitor and remove any duplicate data.
Step 2: Resolve structural errors
You may discover structural errors during data integration. These errors are often tied to incorrect capitalization, naming conventions, and typos. In addition, due to inconsistencies, categories and classes may be mislabeled. For example, both N/A and not applicable can appear, but they must be analyzed as one category.
Step 3: Filter outliers
It is common to find one-off observations that, at first glance, do not seem to fit within the data you are analyzing. The performance of the data will often benefit from removing these one-off outliers, such as an incorrect data entry. However, just because an outlier exists does not mean it is inaccurate. To determine whether it is accurate, you must validate its correctness before removing it.
Step 4: Handle missing data
You cannot ignore missing data because many algorithms will not process missing values. However, several approaches exist to deal with them. For example, if you have null values, you can replace them with NULL or N/A if they have no impact on your analysis.
Step 5: Validate
As a final step in the process, you should be able to validate that the data meets your project's requirements. At this point, you should have the ability to run descriptive statistics on the data to ensure that your dataset meets established best practices. You should also consider visualizing the data to determine any additional insights that might impact your project before proceeding.