How to normalize data without knowing the min and max values? I have a Lending club dataset from Kaggle;
it contains many different columns: there are for example dummy variables, years, amount of the loan...ect
I want to normalize the data in the training and test set but I have to use the Min and Max of the train set to prevent data leakage from the test set.
My question is: if there is, in the test set or even when I try to predict new data point, a value that is greater that the Max value or lower than the Min value and I normalize it using the same values from the train set, is it correct? can I the model process this value normally?

this is the code that I use to normalize

    from  sklearn.preprocessing import MinMaxScaler

    scaler = MinMaxScaler()
    X_train = scaler.fit_transform(X_train)
The minimum and maximum values are just known limits that are parts of the formula that reshapes the distribution of the data, so if a value is bigger than the previously known value the resulting feature scaling (Normalization) will be still appropriate. 

An alternative is z-scores if you don't feel like using minimum and maximum values.

x'= (x-x̄) / σ
Where x is the original feature vector, x̄  is the average of the vector x  is the mean of that feature vector and σ is its standard deviation.