The objective of this competition is to predict the probability that a customer does not pay back their credit card balance amount in the future based on their monthly customer profile. The target binary variable is calculated by observing 18 months performance window after the latest credit card statement, and if the customer does not pay due amount in 120 days after their latest statement date it is considered a default event. 

The dataset contains aggregated profile features for each customer at each statement date. Features are anonymized and normalized, and fall into the following general categories:

- D_* = Delinquency variables
- S_* = Spend variables
- P_* = Payment variables
- B_* = Balance variables
- R_* = Risk variables

with the following features being categorical:

```
['B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_64', 'D_66', 'D_68']
```

Your task is to predict, for each `customer_ID`, the probability of a future payment default (`target = 1`).

Note that the negative class has been subsampled for this dataset at 5%, and thus receives a 20x weighting in the scoring metric.

## Files

*   **train_data.csv** - training data with multiple statement dates per `customer_ID`
*   **train_labels.csv** -  `target` label for each `customer_ID`
*   **test_data.csv** - corresponding test data; your objective is to predict the `target` label for each `customer_ID`
*   **sample_submission.csv** - a sample submission file in the correct format