Uber Nairobi Ambulance Perambulation Challenge
$6,000 USD
Can you use ML to create an optimised ambulance deployment strategy in Nairobi?
1029 data scientists enrolled, 331 on the leaderboard
17 September 2020—24 January 2021
130 days
To those who tried clustering as their solution.
published 19 Jan 2021, 01:44
edited 1 minute later

What's the best lb score you achieved using only clustering and coordinates of crashes? Mine is 44.29.

Note that i don't even use the time component in my current solution meaning that the locations for the ambulances is static time-wise across the submission file.

Hello @blenz

What clustering algorithm did you use ?

I tried Kmeans too but I am getting 105.81

Thinking of ways to improve the score

A default model using kmeans on the crash coordinates with 6 clusters gives around 52.xx. Make sure you're not making a mistake in the creation of the submission file or something

I will do that. Thank you

I have been using the offline scoring function to score my model. I just submitted my submission file to Zindi, and I got 49.xx

yes. with a little tweaking, you should get a lower score.

Hi Blenz, i got 40.42. I modified kmeans by using the geometric median instead means. Kmeans minimizes the squared distances, but the score function is without squares. In fact, it is a well known optimization problem, called the "Multi Source Weber Problem" (MWP).

Thanks for your answer. I've read a little about MWP.

@Marvin27, what library are you using to create your models ?

Hi Bilaerl, because i am a total beginner with python, i started with a pure java program.