What are the methods of data cleansing? The significance of data cleansing

Nowadays, science and technology have developed like never before, and it is for this reason that many science and technology have made great progress. Just in the last few years, a lot of terms have emerged, such as big data, Internet of Things, cloud computing, artificial intelligence, etc. Among them, the popularity of big data is the highest, because many industries have accumulated huge raw data, and data that can be helpful to enterprise decision-making can be obtained through data analysis, and big data technology can be better than traditional data analysis technology.

However, big data is inseparable from data analysis, and data analysis is inseparable from data, and there is a lot of data in the massive amount of data that we need, and there is also a lot of data that we don't need. Just as there is no such thing as a completely pure world, there will be impurities in the data, which requires us to clean the data to ensure the reliability of the data. Generally speaking, there is noise in the data, so how is the noise cleaned?

Generally speaking, there are three methods for cleaning data, namely binning, clustering, and regression. Each of these three methods has its own advantages, and it can clean up all aspects of noise. The so-called binning method is to put the data to be processed into the box according to certain rules, and then test the data in each box, and take the method to process the data according to the actual situation of each box in the data.

Seeing this, many friends only understand a little, but they don't know how to divide the boxes. How do you divide the bins? We can binn by the number of rows recorded, so that each bin has the same number of records.

Or we can set a constant for the range of each box, so that we can sort the boxes according to the range of the box. In fact, we can also customize the interval for binning. All three of these are possible.

Once the box numbers are divided, we can find the average, median, or extreme values of each box to plot a line chart, and generally speaking, the wider the line chart, the more obvious the smoothness.

The regression method and the binning method are equally classic. The regression method uses the data of the function to draw the image, and then smooths the image. There are two types of regression methods, one is unilinear regression and the other is multilinear regression.

Unilinear regression is all about finding the best straight line for two attributes, and being able to get the best line from one property to the other. Multilinear regression is all about finding many properties and fitting the data to a multidimensional surface, so that the noise can be removed.

The workflow of the clustering method is relatively simple, but the operation is really complicated, the so-called clustering method is to group abstract objects into a set of different sets, and find unexpected solitary points in the set, which are noise. This makes it possible to spot the noise directly and then remove it.

We have introduced the methods of data cleaning to you one by one, specifically the binning method, regression method, and clustering method. Each method has its own unique advantages, which also make the data cleansing process smooth. Therefore, mastering these methods will help us in our future data analysis work.

Data cleaning is a very important step in the data analysis process, the purpose of which is to ensure the accuracy, completeness and consistency of the data, so that the subsequent data analysis work can get the correct results. In order to ensure the accuracy of data cleansing results, the following aspects can be carried out:

Set data cleaning rules: Before data cleaning, you need to formulate corresponding data cleaning rules according to the requirements and characteristics of data analysis, including missing value processing, outlier value processing, and duplicate value processing, to ensure that the data cleaning work meets the unified standards.

Carefully review the results of the data cleansing: The results of the data cleansing need to be carefully reviewed for omissions or errors, and the results of the cleansing should be verified to ensure the correctness of the data.

Leverage multiple data analysis methods: The accuracy of the data cleansing results can be further validated by using a variety of different data analysis methods to analyze the data.

Establish data cleansing logs: Establish data cleansing logs to record all data cleansing processes, including data sources, data cleansing rules, data cleansing results, and raw data, which can help troubleshoot data anomalies and trace the root cause of data problems.

Professional technical support: If conditions permit, you can seek professional data cleaning institutions or technical support to ensure the accuracy and reliability of data cleaning work.

In summary, in order to ensure the accuracy of data cleansing results, it is necessary to establish standardized data cleansing rules, conduct careful review, use multiple analysis methods, establish data cleansing logs, and seek professional technical support. Only in this way can we truly ensure the effect of data cleansing, so as to obtain correct data analysis results.

There are industry experts in this area, and we can go to them to solve this problem, and we use unnamed penguins.

Data cleansing, also known as data cleansing, is used to detect and correct (or delete) inaccurate or corrupted records in a recordset, table, or database. Broadly speaking, data purging or purging refers to identifying incorrect, incomplete, irlevant, inaccurate, or otherwise problematic parts of data and then replacing, modifying, or deleting that dirty data.

Significance of data cleansing: In simple terms, data cleansing is often considered to be a useless part (incomplete data that does not affect the result). But it's a valuable process that can help businesses save time and increase efficiency.

Data cleansing is the final process of discovering and correcting identifiable errors in data files, including checking data consistency, handling invalid and missing values, and so on. Unlike questionnaire review, post-entry data cleanup is generally done by a computer rather than a human being.

Data cleansing, as the name suggests, refers to the final process of finding and correcting recognizable errors in data files, including checking data consistency, dealing with invalid and missing values, etc. Because the data in the data warehouse is a collection of subject-oriented data, which is extracted from multiple business systems and contains historical data, it is inevitable that some data is wrong data and some data are in conflict with each other, and these wrong or conflicting data are obviously what we don't want, which is called "dirty data". We need to "wash out" the "dirty data" according to certain rules, which is data cleaning.

The task of data cleaning is to filter the data that does not meet the requirements, and submit the filtering results to the business department to confirm whether it is filtered out or corrected by the business unit before extraction. There are three main categories of data that do not meet the requirements: incomplete data, wrong data, and duplicate data. Data cleaning is different from questionnaire review, and data cleaning after entry is generally done by a computer rather than a manual.

Data cleansing is an important part of data analysis and mining, mainly to deal with invalid, wrong, duplicate or incomplete data, so as to improve the accuracy and usability of data. Here are some of the data cleaning methods that may be used for bumpers:1

Missing value processing: You can choose to delete the data row containing the missing value or fill in the missing value. 2.

Outlier handling: Outliers in the data may affect the accuracy of the analysis results, and you can choose to delete the outliers or correct them in an appropriate way. 3.

Duplicate value processing: Duplicate data may cause bias in the data analysis results, so you can choose to delete the duplicate values or merge them. 4.

Data formatting: Different data types can be standardized, such as date format, numeric format, text format, etc. 5.

Data normalization: For data of different scopes and different units, it can be normalized for comparison and analysis. 6.

Data conversion: Converting data into the desired form or format for analysis using specific algorithms or tools. 7.

Data deduplication: De-duplication ensures the uniqueness of the data, reduces the amount of computation, and improves the efficiency of analysis. These methods may not be applicable to all data cleansing scenarios.