Subscription Churn Risk — Competition

Overview

A subscription business wants to know who is about to leave, early enough to do something about it.

You get 12,000 labelled subscribers and 4,000 unlabelled ones. Predict, for each subscriber in the test set, the probability that they churn within 30 days.

Submit a probability between 0 and 1, not a hard 0/1 label — the metric rewards getting the ordering right, and a hard label throws away all the confidence information you have.

Data

customer_id

Unique subscriber id. Present in both train and test.

tenure_months

Months since the subscription started.

monthly_charges

Current monthly bill, in USD.

support_tickets

Support tickets opened in the last 90 days.

contract_type

One of month_to_month, one_year, two_year.

auto_pay

1 if automatic payment is enabled, else 0.

late_payments

Late payments in the last 12 months.

avg_session_minutes

Mean minutes per session over the last 30 days.

region

One of north, south, east, west.

churned

TARGET. 1 if the subscriber cancelled within 30 days, else 0. Train only.

Rules

Submissions per day: 5, resetting at 00:00 UTC.
Final scoring: pick up to 2 submissions to count. If you pick none, your best public submission is used automatically.
Public vs private: the live leaderboard is scored on ~30% of the test rows. Final standings use the other ~70%, which nobody sees until the competition closes.
Teams: individual entry only for now.
External data: allowed, provided it is public and you say so in your writeup.
One account per person. Multiple accounts, or sharing submissions between accounts, means disqualification.

Submit a CSV with exactly two columns: customer_id and churned, one row for each of the 4,000 test rows.