Data Normalization using Median & Median Absolute Deviation (MMAD) based Z-Score for Robust Predictions vs. Min – Max Normalization

In the world of data analytics, data normalization is not a new concept as it is a preprocessing stage of any type of number driven business problem. The goal of normalization is to change the values of numeric columns in the dataset to a common scale, without distorting differences in the ranges of values. There are multitude of data normalization techniques available namely Min-Max normalization, Z-Score normalization, coefficient based normalization etc. Data normalization may also vary based on the level of measurement of the variables namely nominal scale variables, ordinal scale variable interval scale variable, additive scale variable etc. However, the scope of this paper is purely focused on a continuous set of numbers and deploy the proposed (MMAD) normalization technique to standardize the values for creating a robust simple linear regression model. The alternative aim of this paper is also to pitch the proposed (MMAD) normalization technique against the min-max normalization method to see its effectiveness and robustness.

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.