# Endava Customer Retention Hackathon — Data

来源：https://www.kaggle.com/competitions/endava-customer-retention-hackathon/data

采集时间：2026-10-19T07:41:29Z

This page appears alongside the data files. It describes what files have been provided and the format of each. There is no single format for this page that is appropriate for all competitions, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.

## Files

*   **train_data.csv** - the training set
*   **members.csv** - dataset with information about members
*   **transactions.csv** - dataset with information about transactions
*   **user_logs.csv** - dataset with logs about user behaviour 
*   **kaggle_test_data.csv** - the test set


**train_data.csv** - the train set, containing the user ids and whether they have churned.
- msno: user id
- is_churn: This is the target variable. Churn is defined as whether the user did not continue the subscription within 30 days of expiration. is_churn = 1 means churn,is_churn = 0 means renewal.

**members.csv** - user information. Note that not every user in the dataset is available.
- msno
- city
- bd: age. Note: this column has outlier values ranging from -7000 to 2015, please use your judgement.
- gender
- registered_via: registration method
- registration_init_time: format %Y%m%d

**transactions.csv** - transactions data until 3/31/2017.
- msno: user id
- payment_method_id: payment method
- payment_plan_days: length of membership plan in days
- plan_list_price: in New Taiwan Dollar (NTD)
- actual_amount_paid: in New Taiwan Dollar (NTD)
- is_auto_renew
- transaction_date: format %Y%m%d
- membership_expire_date: format %Y%m%d
- is_cancel: whether or not the user canceled the membership in this transaction.

**user_logs.csv** - daily user logs describing listening behaviors of a user. Data collected until 3/31/2017.
- msno: user id
- date: format %Y%m%d
- num_25: # of songs played less than 25% of the song length
- num_50: # of songs played between 25% to 50% of the song length
- num_75: # of songs played between 50% to 75% of of the song length
- num_985: # of songs played between 75% to 98.5% of the song length
- num_100: # of songs played over 98.5% of the song length
- num_unq: # of unique songs played
- total_secs: total seconds played

 **kaggle_test_data.csv** - the test set with msno for generating predictions
- msno: user id