The mining of data entails converting raw data into useful information that can further analyze and derive critical insights. The raw data you obtain from your source can often be in a cluttered condition that is completely unusable. This data needs to be preprocessed to be analyzed, and the steps for the same are listed below.

Data Cleaning

Data cleaning is the first step of data preprocessing in data mining. Data obtained directly from a source is generally likely to have certain irrelevant rows, incomplete information, or even rogue empty cells.

These elements cause a lot of issues for any data analyst. For instance, the analyst’s platform might fail to recognize the elements and return an error. When you encounter missing data, you can either ignore the rows of data or attempt to fill in the missing values based on a trend or your own assessment. The former is what is generally done.

But a greater problem may arise when you are faced with ‘noisy’ data. To deal with noisy data, which is so cluttered that it cannot be understood by data analysis platforms or any coding platform, many techniques are utilized.

If your data can be sorted, a prevalent method to reduce its noisiness is the ‘binning’ method. In this, the data is divided into bins of equal size. After this, each bin may be replaced by its mean values or boundary values to conduct further analysis.

Another method is ‘smoothing’ the data by using regression. Regression may be linear or multiple, but the motive is to render the data smooth enough for a trend to be visible. A third approach, another prevalent one, is known as ‘clustering.’

In this data preprocessing method in data mining, surrounding data points are clustered into a single group of data, which is then used for further analysis.

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