Normalization

Normalization, also known as feature scaling, is the process of adjusting data values to fit in a prescribed range. The is done to make the Machine Learning process more efficient and accurate.

Numeric Values Normalization

Common forms of numeric value normalization include:

Calendar Date Normalization

Single calendar dates (year/month/day) often have no meaning for training a machine learning model. One way to give them meaning is to calculate a time difference between a date and some other date. This time difference can then have meaning because it can be compared to other time differences in model training and inference processing data.

For example, a model to predict product returns by customers might use calls to customer care as one data feature. If the June 15th date of a call is used for a customer that returns a product on July 15th is used by itself, it isn’t comparable to a customer care call for a product return on August 15. But if the time interval between the call and return is used instead, the 30 day interval is comparable to other returns.

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