Data Cleaning Techniques: Learn Simple & Effective Ways To Clean Data

In this article, we will learn about the different data cleaning techniques and how to effectively clean data using them. Each technique is important and you also learn something new.

Top Data Cleaning Techniques to Learn

Let's understand, in the following paragraphs, the different data cleaning techniques.

Remove Duplicates

The likelihood of having duplicate entries increases when data is collected from many sources or scraped. People making mistakes when keying in the information or filling out forms is one possible source of these duplications.

All duplicates will inevitably distort your data and make your analysis more difficult. When trying to visualize the data, they can also be distracting, so they should be removed as soon as possible.

Remove Irrelevant Data

If you're trying to analyze something, irrelevant info will slow you down and make things more complicated. Before starting to clean the data, it is important to determine what is important and what is not. When doing an age demographic study, for instance, it is not necessary to incorporate clients' email addresses.

There are various other elements that you would want to remove since they add nothing to your data. They include URLs, tracking codes, HTML tags, personal identifiable data, and excessive blank space between text.

Standardize Capitalization

It is important to maintain uniformity in the text across your data. It's possible that many incorrect classifications would be made if capitalization were inconsistent. Since capitalization might alter the meaning, it could also be problematic when translating before processing.

Text cleaning is an additional step in preparing data for processing by a computer model; this step is much simplified if all of the text is written in lowercase.

Convert Data Types

If you're cleaning up your data, converting numbers is probably the most common task. It's common for numbers to be incorrectly interpreted as text, although computers require numeric data to be represented as such.

If they are shown in a readable form, your analytical algorithms will be unable to apply mathematical operations because strings are not considered numbers. Dates that are saved in a textual format follow the same rules. All of them need to be converted into numbers. For instance, if you have the date January 1, 2022, written down, you should update it to 01/01/2022.

Clear Formatting

Data that is overly structured will be inaccessible to machine learning algorithms. If you are compiling information from several resources, you may encounter a wide variety of file types. Inconsistencies and errors in your data are possible results.

Any pre-existing formatting should be removed before you begin working on your documents. This is typically a straightforward operation; programs like Excel and Google Sheets include a handy standardization feature.

Fix Errors

You'll want to eliminate all mistakes from your data with extreme caution. Simple errors can cause you to lose out on important insights hidden in your data. Performing a simple spell check can help avoid some of these instances.

Data like email addresses might be rendered useless if they contain typos or unnecessary punctuation. It may also cause you to send email newsletters to those who have not requested them. Inconsistencies in formatting are another common source of error.

A column containing just US dollar amounts, for instance, would require a conversion of all other currency types into US dollars to maintain a uniform standard currency. This also holds for any other unit of measurement, be it grams, ounces, or anything else.

Language Translation

You'll want everything to be written in the same language so that your data is consistent. Also, most data analysis software is limited in its ability to process many languages because of the monolingual nature of the Natural Language Processing (NLP) models upon which it is based. In that case, you'll have to do a complete translation into a single language.

Handle Missing Values

There are two possible approaches to dealing with missing values. You can either input the missing data or eliminate the observations that contain this missing value. Your decision should be guided by your analysis objectives and your intended use of the data.

The data may lose some valuable insights if you just eliminate the missing value. You probably have your reasons for wanting to retrieve this data in mind. It may be preferable to fill in the blanks by determining what should be entered into the relevant fields. If you don't recognize it, you can always substitute "missing." If it's a number, just type "0" into the blank. However, if too many values are missing to be useful, the entire section should be eliminated.

Conclusion

We reach the final parts of the article, having discussed 8 highly important data cleaning techniques professionals must know about. These techniques make your job easier to deal with data, removing unwanted ones. If you feel data and numbers are where you feel at ease, data science is the ideal career path for you.

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