# BoolArt User Default Prediction — Data

来源：https://www.kaggle.com/competitions/boolart-user-default-prediction/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description

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 norhttps://www.kaggle.com/competitions/boolart-user-default-prediction/submitThe 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.parquet - training data with multiple statement dates per customer_ID

- train_labels.csv - target label for each customer_ID

- test_data.parquet - 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