The data describes 51,685 life assurance policies, each identified by a unique Policy ID. Each year, some policies lapse as clients change jobs, move countries etc. The information on these policies are in multiple files.

Numerical quantities have been transformed, and many categories have been assigned unique IDs in place of the original text.

The objective of this hackathon is to develop a predictive model that determines the likelihood for a customer to churn - to seek an alternative insurer or simply stop paying for insurance altogether.

sample_submission.csv contains rows for the remaining Policy IDs (with ‘?’s in TRAIN). You must predict which of these policies marked with '?' will lapse in 2020.