Categorization, missing values, and normalization

For correct and accurate predictions calculated with machine learning models, the incoming data should be presented in the ideal format. The ideal format means that all values are present in a dataset, numerical data is used in numerical features and not categories or labels, or the distribution of features is even (Gaussian). However, many presumptions are not always true in the real world. For this reason, after basic transformations, such as joining or merging data, are done, we should undertake statistical research that shows the real format of data. Based on statistical research, we will know the difference between the ideal and real format of incoming data. This section will describe techniques used to transform data from its real format to its ideal, comparable, and meaningful format.

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