Measurements with different ranges should be normalized to similar ranges. Measurements of similar ranges will contribute equally to training models. If measurements are left at different ranges they may not contribute equally to the model.
A common normalization is to transform each measurement to a mean of 0 and and standard deviation of 1 by subtracting the mean and dividing by the standard deviation.
x -= x.mean(axis=0) x /= x.std(axis=0) see code
Visualize the normalized data to assure all measures are in comparable ranges to contribute equally to model training. Visualizing data after each step gives confidence your code is working as expected. See notebook code.
References for this and other normalizations
https://towardsdatascience.com/data-normalization-in-machine-learning-395fdec69d02 https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#sphx-glr-auto-examples-preprocessing-plot-all-scaling-py
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