This question already has an answer here:
With feature scaling we just change representation of the data. This can make our model run faster but how this can improve accuracy? It is the same data after all.
When I train my SVM without feature scaling I get an accuracy of %61, after MinMax scaling from sklearn accuracy increases to %96. How is this possible even we work with same data and use the same model?