About data preprocessing

This is an article about data preprocessing.

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Today we will learn about data preprocessing.

Data preprocessing is the first step in data analysis and refers to the process of processing initial data into a form suitable for analysis.

Below is information on data preprocessing.

Data collection

Data preprocessing occurs after data collection and involves preparing the data for use in data collected from various sources.

Missing value handling

There are often missing values ​​in the data, and these missing values ​​need to be handled so that they can be used in analysis.

Methods such as filling or deleting missing values ​​are used.

Outlier handling

Outliers can distort data analysis results, so they must be identified and processed to maintain data accuracy.

Feature selection and extraction

It includes the process of removing unnecessary features for analysis or extracting meaningful features.

Text and image data processing

You need to tokenize, cleanse, and vectorize text data, resize image data, and extract features.

Data Integration

You need to integrate data from multiple sources and remove duplication.

Time series data processing

In the case of time series data, it is necessary to organize the data by time period and extract characteristics over time to analyze patterns over time.

Conclusion

Data preprocessing is a very important step that determines the success or failure of data analysis. It is the process of improving the quality of data and processing it into a form that can be used for analysis.

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