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

London Journal of Research in Science: Natural and Formal
Volume | Issue | Compilation
Authored by Sunil Kappal , NA
Classification: NA
Keywords: Normalization, Regression, Median Absolute Deviation, MMAD
Language: English

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.

               
Google logo

Sorry, the file you have requested does not exist.

Make sure that you have the correct URL and the file exists.

Get stuff done with Google Drive

Apps in Google Drive make it easy to create, store and share online documents, spreadsheets, presentations and more.

Learn more at drive.google.com/start/apps.



author

For Authors

Author Membership provide access to scientific innovation, next generation tools, access to conferences/seminars
/symposiums/webinars, networking opportunities, and privileged benefits.
Authors may submit research manuscript or paper without being an existing member of LJP. Once a non-member author submits a research paper he/she becomes a part of "Provisional Author Membership".

Know more

institutes

For Institutions

Society flourish when two institutions come together." Organizations, research institutes, and universities can join LJP Subscription membership or privileged "Fellow Membership" membership facilitating researchers to publish their work with us, become peer reviewers and join us on Advisory Board.

Know more

subsribe

For Subscribers

Subscribe to distinguished STM (scientific, technical, and medical) publisher. Subscription membership is available for individuals universities and institutions (print & online). Subscribers can access journals from our libraries, published in different formats like Printed Hardcopy, Interactive PDFs, EPUBs, eBooks, indexable documents and the author managed dynamic live web page articles, LaTeX, PDFs etc.

Know more