This K-nearest neighbors lesson is for a beginner. Read the short notes, run each example, then change one name or number and run it again.
K-nearest neighbors predicts a new point from the labels of the closest known points.
k is how many neighbours you ask. An even k can tie, so an odd k is a common choice.
Example 1: Start here
Scale the columns first, or a column with large numbers dominates the distance.
points = [(1, "A"), (2, "A"), (8, "B")] # a list; the values stay in this order
query = 3 # a whole number
nearest = sorted(points, key=lambda item: abs(item[0] - query))[:2] # a new list, sorted
print(nearest)
Example 2: Add two rows
K-nearest neighbors applies one operation to every number. zip pairs the rows, and the list keeps each sum.
left = [1, 2, 31] # a list; the values stay in this order
right = [10, 20, 30] # a list; the values stay in this order
print([a + b for a, b in zip(left, right)]) # pair the lists, then combine each pair
Example 3: Average a column
The average is the total divided by how many numbers you have. That is the same summary a statistics page starts with.
scores = [70, 31, 90] # a list; the values stay in this order
print(sum(scores) / len(scores)) # len counts the items
Example 4: Keep values above a line
A cutoff drops the low values. Change 80 and run the example again to see who remains.
scores = [70, 31, 90] # a list; the values stay in this order
print([score for score in scores if score >= 80]) # keep the items that pass the test
Scale the columns first, or a column with large numbers dominates the distance.
