The train set contains ~100 000 and the test contains ~45 000 survey responses from around Africa and the world.

The objective of this challenge is to build a machine learning model to predict which individuals across Africa and around the world are most likely to be financially resilient or not.

Starter notebooks in Python and R will be provided. These notebooks will show you how to read in the data, build a machine learning model and make a submission on Zindi.

If, when you click to download the starter notebook it takes you to another page, ctrl-S and it will save to your downloads folder. Otherwise, you will be able to find it in the gDrive link shared in the discussion forum.

The target for this challenge is if you were in an emergency and needed to make a payment within the next month, can you?

Files available for download:

• Train.csv - contains the target. This is the dataset that you will use to train your model.
• Test.csv- resembles Train.csv but without the target column. This is the dataset on which you will apply your model to.
• SampleSubmission.csv - shows the submission format for this competition, with the ID column mirrors that of Test.csv and the target column containing your predictions. The order of the rows does not matter, but the names of the ID must be correct.
• VariableDefinitions.csv - A file that contains the definitions of each column in the dataset. For columns(Q1 - Q28), Value 1 - Yes, 2 - No, 3 - Don’t Know 4 - refused to answer
• StarterNotebooks - these are notebooks that show you how to read in the data, build a simple model and make a submission on Zindi.

Data Reference:

Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank. Ref: WLD_2017_FINDEX_v02_M. Accessed at https://globalfindex.worldbank.org on 4 March 2021.