Cleaning

Cleaning refers to the process of identifying, correcting, or removing corrupt, inaccurate, or irrelevant data from a dataset. It is an essential step in the data preparation process, as it ensures that the data is accurate, consistent, and complete. Cleaning involves various techniques such as handling missing values, removing duplicates, correcting inconsistent values, and dealing with outliers. The goal of cleaning is to improve the quality of the data and make it suitable for analysis or modeling. Incomplete or inaccurate data can lead to incorrect conclusions and poor decision-making. Therefore, cleaning is a critical step in any data science project.

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Observations: Observations refer to the act of collecting data or information through various means such as surveys, experiments, or monitoring. In data science and artificial intelligence, observations are used to gather information about a particular p ... Read More >>
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Algorithmic Improvements: Algorithmic improvements refer to the process of enhancing the efficiency and effectiveness of an algorithm by modifying its design or implementation. This can involve reducing the time complexity of an algorithm, improving its accuracy, or ... Read More >>

Cleaning

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