# FAQ: K-Nearest Neighbors - Data with Different Scales: Normalization

#1

This community-built FAQ covers the “Data with Different Scales: Normalization” exercise from the lesson “K-Nearest Neighbors”.

Paths and Courses
This exercise can be found in the following Codecademy content:

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#2

Hi!

Why is my slightly different answer not accepted? It’s the rounding probably, but I believe it’s the same:

``````def min_max_normalize(lst):
minimum = min(lst)
maximum = max(lst)

normalized = []
for item in lst:
normalized.append(1- (maximum - item) / (maximum - minimum))
return normalized
``````

#3

late reply but in case you haven’t figured it out yet, it’s because you’re using the incorrect normalization formula. which is (value - max) / (max - min).

So your code should have been

for item in lst:
normalized.append((item - minimum) / (maximum - minimum))
return normalized