This is a private hackathon whose primary purpose is for the members of the DSN Ai+ Club Ibadan to apply what they have learnt. If you are part of Ai+ Club Ibadan contact the club leader for the secret code.

You may have seen recent news articles stating that air quality has improved due to COVID-19. This is true for some locations, but as always the truth is a little more complicated. In parts of many African cities, air quality seems to be getting worse as more people stay at home. For this challenge we’ll be digging deeper into the data, finding ways to track air quality and how it is changing, even in places without ground-based sensors. This information will be especially useful in the face of the current crisis, since poor air quality makes a respiratory disease like COVID-19 more dangerous.

We’ve collected weather data and daily observations from the Sentinel 5P satellite tracking various pollutants in the atmosphere. Your goal is to use this information to predict PM2.5 particulate matter concentration (a common measure of air quality that normally requires ground-based sensors to measure) every day for each city. The data covers the last three months, spanning hundreds of cities across the globe.

About DSN Ai+ Club Ibadan (twitter.com/dsn_ui):

DSN Ai+ Club Ibadan (University of Ibadan, Nigeria) is a branch of Data Science Nigeria Community aimed at building and sustaining Ai talents at the university with the vision #1millionAiTalentsIn10Years.

This is a private hackathon whose primary purpose is for the members of the DSN Ai+ Club Ibadan to apply what they have learnt. If you are part of Ai+ Club Ibadan contact the club leader for the secret code.

Teams and collaboration

You may participate in this competition as an individual.

Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be disqualified.

Code must not be shared privately outside of a team. Any code that is shared, must be made available to all competition participants through the platform. (i.e. on the discussion boards).

Datasets and packages

The solution must use publicly-available, open-source packages only. Your models should not use any of the metadata provided.

You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.

If the challenge is a computer vision challenge, image metadata (Image size, aspect ratio, pixel count, etc) may not be used in your submission.

If external data is allowed you may only use data that is freely available to everyone. You must send it to Zindi to confirm that it is allowed to be used and then it will appear on the data page under additional data.

You may use pretrained models as long as they are openly available to everyone.

The data used in this competition is the sole property of Zindi and the competition host. You may not transmit, duplicate, publish, redistribute or otherwise provide or make available any competition data to any party not participating in the Competition (this includes uploading the data to any public site such as Kaggle or GitHub). You may upload, store and work with the data on any cloud platform such as Google Colab, AWS or similar, as long as 1) the data remains private and 2) doing so does not contravene Zindi’s rules of use.

You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the competition data, and work with Zindi to rectify any unauthorised transmission or access.

Your solution must not infringe the rights of any third party.

Submissions and winning

You may make a maximum of 30 submissions per day.

Zindi maintains a public leaderboard and a private leaderboard for each competition. The Public Leaderboard includes approximately 50% of the test dataset. While the competition is open, the Public Leaderboard will rank the submitted solutions by the accuracy score they achieve. Upon close of the competition, the Private Leaderboard, which covers the other 50% of the test dataset, will be made public and will constitute the final ranking for the competition.

You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this competition constitutes your acceptance of these official competition rules.

Please refer to the FAQs and Terms of Use for additional rules that may apply to this competition. We reserve the right to update these rules at any time.

The error metric for this competition is the Root Mean Squared Error

Submissions should follow the sample submission format, with ‘Place_ID X Date’ in one column and predictions for ‘target’ in the other.

Place_ID X Date        target
0OS9LVX X 2020-01-02     2
0OS9LVX X 2020-01-03     91
0OS9LVX X 2020-01-04     34

Competition closes on 26th July 2020.

Final submissions must be received by 17:00 PM GMT.

We reserve the right to update the contest timeline if necessary.