There are three main approaches to cleaning
Posted: Thu Jan 30, 2025 10:57 am
Data for analytics or model training is huge samples. Removing "garbage" from hundreds of thousands of values manually is difficult, and sometimes impossible, so most often the process is automated.
Fully automated cleaning - the specialist uses Big Data tools that are built into the database management system, such as Apache Hive, or cleans the data using analytical systems such as SAS or IBM SPSS.
Cleaning with scripts - a specialist writes scripts himself, for example in Python . These scripts process data and clean it according to specified rules.
Manual cleaning - the specialist himself looks through the sample and removes errors. This method is used very rarely, usually on small samples or as an auxiliary one.
During cleaning, a specialist or program uses different bulgaria telegram data methods - for example, some data is corrected, some is erased from the database. Here are some examples of what can be done with data during cleaning.
Delete. If the data is duplicated or contradictory, it is deleted according to some algorithm. For example, for duplicates, you can leave only the first or only the last copy of the record. And for contradictions - only one of the values.
Compare. This method is used when information differs in different places. The data is compared according to a number of criteria - as a result, the value similar to the real one is selected and substituted instead of the incorrect one.
Let's say the same user's phone number is listed differently in two different places. You can look at how that phone number is listed in a third place and figure out which value is correct.
Correct. To replace data, you don't always need to compare it with other values from the database. For example, typos in words are corrected using a dictionary - it describes how to spell a word correctly. And obvious "outliers" are replaced with some average value.
Let's say there is a bracket in place of a person's name. This is clearly an error - the data was calculated incorrectly. You can substitute some average statistical value for the name, for example, "Tatyana Kuznetsova".
Fully automated cleaning - the specialist uses Big Data tools that are built into the database management system, such as Apache Hive, or cleans the data using analytical systems such as SAS or IBM SPSS.
Cleaning with scripts - a specialist writes scripts himself, for example in Python . These scripts process data and clean it according to specified rules.
Manual cleaning - the specialist himself looks through the sample and removes errors. This method is used very rarely, usually on small samples or as an auxiliary one.
During cleaning, a specialist or program uses different bulgaria telegram data methods - for example, some data is corrected, some is erased from the database. Here are some examples of what can be done with data during cleaning.
Delete. If the data is duplicated or contradictory, it is deleted according to some algorithm. For example, for duplicates, you can leave only the first or only the last copy of the record. And for contradictions - only one of the values.
Compare. This method is used when information differs in different places. The data is compared according to a number of criteria - as a result, the value similar to the real one is selected and substituted instead of the incorrect one.
Let's say the same user's phone number is listed differently in two different places. You can look at how that phone number is listed in a third place and figure out which value is correct.
Correct. To replace data, you don't always need to compare it with other values from the database. For example, typos in words are corrected using a dictionary - it describes how to spell a word correctly. And obvious "outliers" are replaced with some average value.
Let's say there is a bracket in place of a person's name. This is clearly an error - the data was calculated incorrectly. You can substitute some average statistical value for the name, for example, "Tatyana Kuznetsova".