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)