Which Delivery Zone?

10 pts · 10 coins

Machine Learning in CodeEasy

Which Delivery Zone?

A courier company splits a town into a "north" and a "south" zone. You have the map positions of some known addresses (points, as [x, y] in kilometres) and their zones (zones). Both columns are in kilometres, so no scaling is needed.

Write zone_for(points, zones, new_points, k).

Fit a KNeighborsClassifier(n_neighbors=k) on the known addresses.
Predict the zone for every point in new_points, in order.

Return a list of zone names (strings).

Examples

points = [[1.2, 8.5], [2.1, 9.3], [0.8, 7.6], [1.9, 8.1], [7.4, 2.2], [8.3, 1.5], [6.9, 3.1], [7.8, 0.9], [4.6, 6.2], [5.3, 4.4]]

zones = ['north', 'north', 'north', 'north', 'south', 'south', 'south', 'south', 'north', 'south']

new_points = [[2.0, 7.0], [6.5, 2.5]]

k = 3

returns ['north', 'south']

points = [[1.2, 8.5], [2.1, 9.3], [0.8, 7.6], [1.9, 8.1], [7.4, 2.2], [8.3, 1.5], [6.9, 3.1], [7.8, 0.9], [4.6, 6.2], [5.3, 4.4]]

zones = ['north', 'north', 'north', 'north', 'south', 'south', 'south', 'south', 'north', 'south']

new_points = [[4.9, 5.6]]

k = 1

returns ['north']

Hints

solution.py

1from sklearn.neighbors import KNeighborsClassifier
2
3
4def zone_for(points, zones, new_points, k):
5    pass
6