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”.

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This exercise can be found in the following Codecademy content:

Data Science

Machine Learning

FAQs on the exercise Data with Different Scales: Normalization

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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