Data cleaning methods include: 1. Boxing method, put the data to be processed into boxes according to certain rules, and then test the data in each box, and based on the actual performance of each box in the data The situation is followed by methods to process the data. 2. The regression method uses the function data to draw the image, and then smoothes the image. 3. Clustering method.
The operating environment of this tutorial: Windows 7 system, Dell G3 computer.
Nowadays, science and technology have achieved unprecedented development. It is for this reason that many science and technologies have made substantial progress. Just in the past few years, many terms have appeared, such as big data, Internet of Things, cloud computing, artificial intelligence, etc. Among them, big data is the most popular. This is because many industries have accumulated huge amounts of raw data. Through data analysis, data that is helpful for corporate decision-making can be obtained, and big data technology can be better than traditional data analysis technology. .
However, big data cannot be separated from data analysis, and data analysis cannot be separated from data. There is a lot of data we need in the massive data, and there is also a lot of data we don’t need. Just as nothing in the world is completely pure, there will also be impurities in 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? In this article, we will introduce to you the method of data cleaning.
Generally speaking, there are three methods for cleaning data, namely binning method, clustering method and regression method. Each of these three methods has its own advantages and can clean up the noise in an all-round way.
The binning method is a frequently used method. The so-called binning method is to put the data that needs to be processed into boxes according to certain rules, and then test each box. data, and adopt methods to process the data according to the actual situation of each box in the data. Seeing this, many friends only understand it a little bit, but don’t know how to divide it into boxes. How to divide it into boxes? We can binning according to the number of rows of records so that each box has the same number of records.
Or we can set a constant for the interval range of each box, so that we can divide the bins according to the range of the interval. In fact, we can also customize the interval for binning. All three methods are possible. After dividing the box numbers, we can find the average and median of each box, or use extreme values to draw a line chart. Generally speaking, the greater the width of the line chart, the more obvious the smoothness.
The regression method uses the function data to draw the image, and then smoothes the image. There are two types of regression methods, one is single linear regression and the other is multilinear regression. Single linear regression is to find the best straight line between two attributes, which can predict one attribute from the other. Multilinear regression is to find many attributes to fit the data to a multidimensional surface, so that noise can be eliminated.
The workflow of the clustering method is relatively simple, but the operation is indeed complicated. The so-called clustering method is to group abstract objects into different sets, and find the Collecting unexpected isolated points, these isolated points are noise. In this way, you can directly find the noise and then remove it.
We have introduced to you one by one the methods of data cleaning, specifically the binning method, regression method and clustering method. Each method has its own unique advantages, which also allows the data cleaning work to proceed smoothly. Therefore, mastering these methods will help us in subsequent data analysis work.
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