The data describes ~17,000 policies sold by AXA for car insurance. Information on the car type, make, customer age and start of policy are in the data.

The objective of this hackathon is to develop a predictive model that determines if a customer will submit a claim within 3 months from their first transaction.

A Python starter notebook will be provided. The notebook will teach you how to load the data, explore the data, make a simple machine learning model and make a submission file which you will submit on Zindi.

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-related columns. 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 mirroring 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.
• VariableDescription.csv - contains definitions of the data headers
• StarterNotebook.ipynb - this Python starter notebook will help you make your first submission onto the leaderboard. Download the data and run this notebook on your local machine and you will be able to make a submission on the leaderboard.