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Introduction To Data Cleaning
Introduction To Data Cleaning

Data Science

Introduction To Data Cleaning

Data cleaning is one of the most important stages in data science. In this article you will learn the basics and benefits of data cleaning.

Before you start reading this article, you can prepare yourself by reading the basics of the data analysis article.

What is Data Cleaning?

Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.

The biggest factor in creating false, blank data is combining multiple data sets. It is important to clean up your data to analyze it correctly.

Data cleaning is the 3rd phase of data analysis and, according to many data scientists, one of the longest.

Steps Of Cleaning Data

Data cleaning steps may vary by project, the steps here are to create a framework for you. Many data sets can be cleared through these steps.

Step 1 – Remove Irrelevant Observations

Clear any renewed or irrelevant observations that may occur during data collection, briefly unwanted observations.

Repeated observations can often occur when multiple data sets are combined, when you scrape data, or when you receive more than one data set.

Let’s give an example of irrelevant observations, you want to analyze the change in customer satisfaction over 12 months, but your data set includes older months.

By removing this data, you can streamline your analysis and save time.

Step 2 – Fix Structural Errors

Structural errors, incorrect naming, typos or capitalization issues. All of these inconsistencies can result in incorrect categories and classes.

For example, “Na” and “Not Applicable” etc. Both of the statements may have been used, these two statements should be examined under the same category.

Step 3 – Fix Unwanted Outliers

Sometimes, there will be one-off observations where, at a glance, they do not appear to fit within the data you are analyzing.

If you have a legitimate reason to remove an outlier, like improper data-entry, doing so will help the performance of the data you are working with.

Note: Having an outlier in the dataset does not mean it is incorrect data, so consider removing it if it is unrelated to the analysis.

Step 4 – Clear Missing Data

Missing data can be the biggest problem of a dataset, maybe overlook the problems in other steps, but missing data is a big problem for algorithms.

Many algorithms will not accept missing data, there are several ways to deal with missing data, let’s examine these 2 ways.

1- As the first option, you can drop the missing value, analysis, data. however, this will reduce your data and prevent your project from using the data here.

2 – You can fill the data with the help of other data, it does not give a 100% result, but average data can be useful for your project.

Final Step – Control to Data

When you say everything is ok, we have one more step. How accurate is the work we do? In this step, we will try to find an answer to this question.

We need 5 sub-questions to answer this question. If you can answer these questions, then the data cleaning process is complete.

  • Does the data make sense?
  • Does the data follow the appropriate rules for its field?
  • Does it prove or disprove your working theory, or bring any insight to light?
  • Can you find trends in the data to help you form your next theory?
  • If not, is that because of a data quality issue?

Data Cleaning Benefits

Data cleaning it provides many benefits for your project, increase efficiency and performance. Correcting errors in the data provides better analysis.

Some machine learning algorithms use data cleaning to get better results. Many reasons like this lead us to data cleaning.

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