nearest_neighbors.py

import numpy as np
from sklearn.neighbors import NearestNeighbors

# Rows = patrons (anonymized), columns = books, values = checkout counts
checkout_matrix = np.array([
    [3, 0, 1, 0, 2],
    [0, 4, 0, 1, 0],
    [2, 0, 3, 0, 1],
    [0, 1, 0, 5, 0],
])

model = NearestNeighbors(n_neighbors=2, metric="cosine")
model.fit(checkout_matrix)

# Find patrons with similar borrowing patterns to patron index 0
distances, neighbor_indices = model.kneighbors(
    [checkout_matrix[0]], n_neighbors=3
)

print("Most similar patron profiles:", neighbor_indices)