What Transformation is
Transformation is a process that involves taking a set of data and changing it in some way. It is often used to make data easier to analyze, or to make it conform to assumptions of a given statistical test. There are several steps involved in transforming data, including:
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Identifying and assessing the quality of the data: Before transforming data, it is important to check for missing values, outliers, and other irregularities.
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Selecting a transformation: Depending on the type of data and the analysis to be undertaken, different transformations may be appropriate. Common transformations include logarithmic, square root, and reciprocal.
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Applying the transformation: The selected transformation is applied to the data.
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Evaluating the results: Once the transformation is applied, the results of the transformation should be evaluated to ensure that it was successful. This step typically involves plotting the transformed data to check for normality.
Examples
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Feature scaling is a form of transformation used to standardize the range of independent variables or features of data.
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Principal component analysis (PCA) is a transformation technique used to reduce the number of variables in a dataset by projecting the data onto a lower dimensional space.
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Data normalization is a transformation technique used to rescale one or more attributes in a dataset to a specified range.
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Log transformation is a transformation technique used to normalize skewness in a dataset.
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Box-Cox transformation is a transformation technique used to normalize skewness in a dataset.