Predicting Branch Visits and Credit Card Up-selling using Temporal Banking Data

Code

• github.com/SandraMNE/ECMLChallenge2016
  OfficialIn papernone★ 0

Abstract

There is an abundance of temporal and non-temporal data in banking (and other industries), but such temporal activity data can not be used directly with classical machine learning models. In this work, we perform extensive feature extraction from the temporal user activity data in an attempt to predict user visits to different branches and credit card up-selling utilizing user information and the corresponding activity data, as part of ECML/PKDD Discovery Challenge 2016 on Bank Card Usage Analysis. Our solution ranked 4 for Task 1 and achieved an AUC of 0.7056 for Task 2 on public leaderboard.

Tasks

Reproductions