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

Description of the challenge:

Recently, there has been an increase in the number of building collapse in Lagos and major cities in Nigeria. Olusola Insurance Company offers a building insurance policy that protects buildings against damages that could be caused by a fire or vandalism, by a flood or storm.

You have been appointed as the Lead Data Analyst to build a predictive model to determine if a building will have an insurance claim during a certain period or not. You will have to predict the probability of having at least one claim over the insured period of the building.

The model will be based on building characteristics. The target variable, Claim, is a:

• 1 if the building has at least a claim over the insured period.
• 0 if the building doesn’t have a claim over the insured period.

About DSN Ai+ Club Unilorin (twitter.com/AiUnilorin):

DSN Ai+ Club Unilorin (University of Ilorin, Nigeria) is a branch of Data Science Nigeria Community aimed at building and sustaining Ai talents at the university with the vision #1millionAiTalentsIn10Yrs.

Teams and collaboration

You may participate in this competition only 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. 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 100 submissions per day.

Note that there is 50% public/private leaderboard split for this challenge.

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 evaluation metric for this competition is the Area Under the ROC curve (AUC).

Your sample submission should look like:

Customer Id  Claim
H0             1     
H10000         0
H10001         1

Competition closes on 18 June 2020.

Final submissions must be received by 3pm GMT.

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