How to Normalize & Scale a Single Data Point
You need to normalise the input in the same way that the training data was normalised -- however, you don't need access to this training data during predictions of new data. If you have used a MinMaxScaler for example, then you can re-use this to transform your new data point:

scaler = MinMaxScaler()
X_normalised = scaler.fit_transform(X)

# do other stuff here like train &amp; validate your model

# now we have a new test data point from somewhere, we scale
# it using the scaler we fitted on the training data
new_test_point_normalised = scaler.transform(new_test_point)

# now we can classify it with our model!


You can serialise the scaler to disk using pickle and re-load it when predicting new data points if necessary (i.e., the model training and testing happen in two different scripts).