Transformation

Transformation refers to the process of converting raw data into a more useful and meaningful form. In data science, transformation is a crucial step in data preprocessing, where data is cleaned, normalized, and standardized to make it suitable for analysis. Transformation can involve various techniques such as scaling, normalization, aggregation, and feature extraction. Scaling involves rescaling the data to a specific range, while normalization involves transforming the data to have a standard distribution. Aggregation involves combining multiple data points into a single value, while feature extraction involves selecting the most relevant features from the data. Transformation is an iterative process that requires careful consideration of the data and the analysis goals.
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