For a 'customers like you' feature, find which customer is most similar to customer 0 by cosine similarity (dot product of the rows ÷ the product of their lengths). Store that customer's row number in result (ignore customer 0 itself).
import numpy as np
# rows = customers, columns = how much they spent in 5 categories
spend = np.array([
[500, 0, 200, 0, 100],
[450, 50, 180, 0, 120],
[0, 600, 0, 300, 0],
[100, 500, 50, 350, 20],
[480, 20, 0, 400, 90],
])Store your final answer in a variable named result.
Each hint gives a little more help. Try on your own first.
Run your code to see the output here.