Overview
Curriculum
Data normalization and scaling are techniques used to adjust the values of a dataset so that they are on a similar scale and have the same properties. This is important because many statistical and machine learning algorithms assume that the data is normally distributed, or that the features are on a similar scale. If this assumption is not met, the algorithms may produce biased or inaccurate results. By normalizing or scaling the data, the data is transformed into a consistent and interpretable form that is suitable for further analysis and modeling. This is an important step in the data science process that helps to ensure the validity and accuracy of the results obtained from any further analysis or modeling.
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