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Data wrangling is the process of cleaning, transforming, and mapping raw data into a more suitable format for analysis. It involves identifying and correcting errors in the data, handling missing or incomplete data, and converting data into a standardized format. Data wrangling is an important step in the data analysis process as it ensures that the data is accurate, consistent, and complete. It also helps to make the data more accessible and easier to work with, which can save time and improve the accuracy of the analysis. Data wrangling can be a time-consuming and complex process, but it is essential for ensuring that the data is of high quality and can be used effectively for analysis.

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Data Archiving: Data archiving is the process of moving data that is no longer actively used to a separate storage device for long-term retention. This data is still important and necessary for future reference or compliance reasons, but it is not needed f ... Read More >>
Equal Frequency Binning: Equal Frequency Binning is a data preprocessing technique used in data science and machine learning to transform continuous variables into categorical variables. It involves dividing a continuous variable into a set of intervals or bins, wh ... Read More >>
Prototypes: Prototypes are preliminary versions of a product or system that are used to test and validate design concepts. In the context of data science and artificial intelligence, prototypes are often used to test and validate machine learning model ... Read More >>

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