What is Data Cleaning?
Data Cleaning in Machine Learning and Data Science
Data cleaning is an important topic when it comes to machine learning and data science. Data cleaning is the process of correcting or deleting incorrect, corrupted, poorly formatted, duplicate, or incomplete data from a dataset, as the name implies.
In this article, I would be giving you a detailed explanation about Data Cleaning.
What is data cleaning?
Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining several data sources, it is possible for data to be duplicated or incorrectly classified in a number of ways.
How do you clean data?
While the procedures used for data cleansing may differ depending on the types of data stored by your firm, you may utilise these fundamental stages to create a framework for your organisation.
Remove duplicate values
Remove any dupliacte values from your dataset. Duplicate observations are most likely to occur during data collecting. When you combine data sets from several sources, scrape data, or obtain data from clients or various departments, duplicate data may result.
Filter unwanted outliers
An outlier is an incorrect data input which should be removed from dataset it will reduce the accuracy of the model. But occasionally, the existence of an outlier could support a theory you’re considering. Remember that the existence of an outlier does not imply that it is erroneous. This step is required to determine the accuracy of the number. You should delete an outlier if it appears to be a mistake this would affect the model accuracy.
Fix structural errors
When you measure or transfer data, you may detect unusual naming practises, typos, or wrong capitalization. These differences could lead to groups or classes being misclassified. For example, “1” and “1.0” may both exist, but they should be examined as one category.
Handle missing data
Since many algorithms do not tolerate missing values, missing data cannot be disregarded. There are several techniques to handle missing data.
- As a first alternative, you may eliminate observations with missing values; however, doing so will result in the loss of information, so keep this in mind before you do so.
- As a second approach, you may fill in missing values based on other observations; however, there is a risk of losing data integrity since you may be acting on assumptions rather than real facts.
- As a third option is to alter how the data is used in order to more effectively navigate null values.
Advantages of Data Cleaning
Data cleaning is very important before we can use it on our model.
- Merging various datasets creates redundancies and duplicates in the data, which must be deleted.
- However, the drop in model accuracy is the least of the issues that might arise when dirty data is employed directly.
- When noise is constant across the training and testing sets, models trained on raw datasets are compelled to include noise as information, which may lead to accurate predictions.
- Inaccurate and inadequately gathered datasets can lead to models learning incorrect data representations, decreasing their decision-making capacity.
If you like my article and efforts towards the community, you may support and encourage me, by simply buying coffee for me
Conclusion
In this article, I’ve covered what data cleaning is, its benefits, and some tips to clean data. So, I hope you guys have a good understanding of data cleaning today. In the near future, I’ll be writing more articles in which I’ll explain more models and how to implement data cleaning using python libraries with source code.
