When combining data from many sources, there is a high risk of duplication and incorrect labeling. The algorithms could provide wildly different outcomes even when the data is correct. This makes data cleaning a critical requirement in data management. The term “data cleaning” refers to the process of rectifying or removing any incorrect, corrupted, incorrectly formatted, missing, or duplicate information from a dataset There is no universally applicable technique for prescribing the specific procedures involved in data cleaning since the methodology varies from one dataset to another. Data cleaning may be tedious, but following a plan can guarantee consistent results.
Data Cleaning Use cases
You may develop a framework for your business using these simple procedures, despite the fact that the techniques of data cleansing will differ based on the kind of data your organization maintains.
1. Filter duplicate or unimportant data
When data is analyzed in the form of data frames, there are often duplicates throughout columns and rows that must be filtered out or removed. For example, when the same person participates in a survey many times, or when the survey covers various topics on the same subject, generating similar replies from many respondents, duplicates occur.
2. Fix grammatical and syntax errors
Data collected from different sources can have grammatical and syntax errors because the data may be input by different people or systems. Fixing common syntax errors like dates, birthdays, and ages is easy, but fixing errors in spelling takes more time.
3. Remove unnecessary outliers
Outliers must be filtered away before further processing data. Spotting outliers is the most difficult compared to other types of data errors. A data point or group of data points often requires extensive examination before being classified as an outlier. Particular models with an extremely low outlier tolerance may be readily affected by a substantial number of outliers, diminishing the predictions’ quality.
4. Managing missing data
The data can go missing when the data collection is poor. These artifacts are simple to spot, but filling in missing sections unexpectedly affects model quality. Hence cleaning such data to identify missing information becomes absolutely necessary.
5. Validate the accuracy of the data
To make sure that the data being handled is as precise as possible, the accuracy of the data needs to be checked by doing cross-checks within the columns of the data frame. Yet, it is difficult to estimate how accurate data is, and this is only achievable in specific domains where a specified understanding of the data is available.
Data cleaning is a laborious operation in every machine learning project that consumes a substantial portion of the available time. Furthermore, the reliability of the data is the single most crucial factor in the algorithm’s performance, making it the essential aspect of the project
