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I built a linear multivariable regression model from normalized data (for the interval [0; 1]). Initially, the data was not normalized, I normalized the data by myself (independent and dependent variables). I want to use this model to make predictions from newly received data (I get the values ​​of independent variables and I want to predict the value of the dependent variable). The problem is that the data comes in a raw, unnormalized form.

  1. How can I normalize newly arriving data if only one "observation" is received?
  2. What if I want to get the real values ​​of the dependent variable using my model, and not the normalized ones?
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So, the question asks:

  1. How to normalise incoming (individual observations)
  2. How to get the real value predictions and not the normalised values.
  1. When we do normalisation using the Sci-kit learn module, instead of using the very handy fit_transform() method in the scaler, you could instead perform a .fit() over your original observations and then apply the .transform() to the newly-observed values. Obviously, in this case you need separate scalers foreach feature, as they are distributed differently from one another.

  2. Again in Sci-kit learn, there is an inverse_transform() method which reverts the normalised value back to the original scale.

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